
Sulfur dioxide is generally used in wine and vinegar production. It is employed to decrease the bacteria' growth, improve the wines' aroma (since it supports the extraction of polyphenols during maceration), protect the wines from chemical oxidation and the musts from chemical and enzymatic oxidation (blocking free radicals and oxidase enzymes such as tyrosinase and laccase). The composition and storage conditions (i.e., pH, temperature, and alcohol levels) affect oenological results. In various countries, competent authorities have imposed legal limits since it can have toxic effects on humans. It is crucial to dose SO2 levels to allow vinegar production and compliance with legal limits. The iodometric titration named "Ripper test" is the legal method used to dose it in vinegar. In this work, an automatized colorimetric test was validated using the international guidelines ISO/IEC (2017) to allow its use instead of the Ripper test. The test reliability was verified on white, red, and balsamic vinegar with low or high SO2 content. The automatized test showed linearity, precision, and reproducibility similar to the Ripper test, but the accuracy parameter was not respected for the vinegar with a low concentration of SO2. Therefore, the automatized colorimetric test can be helpful to dose SO2 in vinegar with high concentrations of SO2.
Citation: Irene Dini, Antonello Senatore, Daniele Coppola, Andrea Mancusi. Validation of a rapid test to dose SO2 in vinegar[J]. AIMS Agriculture and Food, 2023, 8(1): 1-24. doi: 10.3934/agrfood.2023001
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Sulfur dioxide is generally used in wine and vinegar production. It is employed to decrease the bacteria' growth, improve the wines' aroma (since it supports the extraction of polyphenols during maceration), protect the wines from chemical oxidation and the musts from chemical and enzymatic oxidation (blocking free radicals and oxidase enzymes such as tyrosinase and laccase). The composition and storage conditions (i.e., pH, temperature, and alcohol levels) affect oenological results. In various countries, competent authorities have imposed legal limits since it can have toxic effects on humans. It is crucial to dose SO2 levels to allow vinegar production and compliance with legal limits. The iodometric titration named "Ripper test" is the legal method used to dose it in vinegar. In this work, an automatized colorimetric test was validated using the international guidelines ISO/IEC (2017) to allow its use instead of the Ripper test. The test reliability was verified on white, red, and balsamic vinegar with low or high SO2 content. The automatized test showed linearity, precision, and reproducibility similar to the Ripper test, but the accuracy parameter was not respected for the vinegar with a low concentration of SO2. Therefore, the automatized colorimetric test can be helpful to dose SO2 in vinegar with high concentrations of SO2.
Sulfur dioxide (SO2) is a colorless gas, pungent, toxic, and suffocating [1]. It is obtained by burning sulfur and pyrites. It becomes liquid below 10 ℃ [2]. The use of sulfur dioxide in food processing is very complex [3]. It is used as an antiseptic to inhibit the development of microorganisms. It is more effective against bacteria than against yeasts. Its effect is directly proportional to the dose of use and inversely proportional to the level of contamination [4]. It is employed as an antioxidant compound. It can protect wines from chemical oxidation blocking oxygen and protect musts before fermentation, inhibiting the action of oxidase enzymes (tyrosinase and laccase). The SO2 protects the aroma of wines and favors the extraction of intracellular components such as anthocyanins and polyphenols when added to grapes before maceration [5]. The dosages of SO2 in wines and vinegar vary depending on the composition and storage conditions. A low pH, high temperature, and high alcohol content increase the active sulfur molecule fraction. A high salt concentration decreases the concentration of molecular sulfur dioxide [6]. The SO2 in vinegar production is limited since sulfur dioxide can have toxic effects on humans and damage the wine, interfering with the aromatic baggage and causing the attenuation of the aromas. The World Health Organization has included sulfur dioxide among the preservatives (E220) and indicated the dose of 0.7 mg/kg of body weight as a dose allowable daily [7]. In addition to the toxic effect, sulfur dioxide also can have an allergenic action, so since November 2005, with the entry into force in Europe of the EC Directive no.89/2003 ("allergens directive"), it has become mandatory to report the presence of sulfites and sulfur dioxide in wine and in any other food, when the concentration exceeds 10 mg/L or 10 mg/kg, expressed as SO2 [8]. Currently, national and community legislation sets legal limits on the presence of sulfites in wines and vinegar. European legislation sets maximum limits of 160 mg/L for reds and 210 mg/L for whites, with exceptions that allow the Member State to raise the maximum value of 40 mg /L in unfavorable years. The European limit (160 mg/L) must be observed for red wines in Italy, while the more restrictive national limit (200 mg/L) applies to whites. Higher values apply to sweet wines [9]. The Ripper test is the official method used to determine SO2. It consists of directly titrating the vinegar with iodine using starch as an indicator [10]. This test is cheap, but its sensibility can be affected by iodine which can interfere with ascorbic acid, and it can be performed only by experienced personnel. Some new methods have been proposed to speed up the work in the laboratory [3,11], which require validation processes before they can be used in legal analyses. The development of analytical methods in food control, particularly in the case of legislative compliance valuation, needs the demonstration that they are "appropriate for purpose" through method validation [12]. The critical aspect is to confirm method applicability by providing test reliability and suitability in complex food matrices [13,14,15,16,17,18,19]. This work aims to validate an automated colorimetric test to dose sulfur dioxide in vinegar.
EnzytecTM liquid SO2 Cod. E28600 was purchased from R-Biopharm AG (Darmstadt, Germany). The distilled water was obtained from Sigma-Aldrich (Milan, Italy). The sulphuric acid solution (25%) was prepared from the concentrated acid (Sigma-Aldrich). EDTA, potassium iodide, potassium iodate, hydrogen peroxide, and starch were acquired from Merck. Co. (Darmstadt, Germany). Iodine was provided by Acros Organics (Geel, Belgium).
Three commercial vinegar types were tested: white, red, and balsamic wine vinegar.
The analyzer iCubio iMagic M9, fully automatized, was used to detect the total SO2 content in vinegar. The apparatus pipette reagents and samples into the cuvette, allow the incubation at a controlled temperature, read absorbance at the specific wavelength, and calculate the concentration of the SO2 by a calibration curve. The parameter used in automated photometric systems were: temperature 20 to 37 ℃; wavelengths 340 nm (±5 nm); optical path 1 cm; reaction 10 min (20–25 ℃) or 5 min (37 ℃).
The method reported in the kit instruction (EnzytecTM liquid SO2) was respected. The kit contained: Buffer: two vials ≥ 100 mL; Chromogen: two vials ≥ 25 mL and Calibrator (SO2 = 150 mg/L): one vial ≥ 3.5 mL.
The first step consisted of preparing Reagent 1 by mixing 2000 μL of reagent (Buffer) together with 2000 μL calibrator solution and 2000 μL of the sample, after three minutes, the absorbance was read. Successively, Reagent 2 was obtained by mixing 500 μL of reagent (chromogen) together with 500 μL calibrator solution and 500 μL of sample. After 20 minutes (25 ℃), the absorbance was read.
Enzytec fluid Acid combination Standard (ID-No 5460, 3 × 3 mL) was used to calibrate the automated photometric systems.
The "Ripper" method was performed according to the Portuguese regulation (IPQ 1987), based on a procedure from the Organisation Internationale de la Vigne et du Vin [20]. Vinegar (10.00 mL) was put into an Erlenmeyer flask (V = 500mL), an aliquot of 1% w/v starch indicator (5.00 mL), and a sodium hydrogen carbonate were added. After ten minutes, 5.00 mL of 33% (v/v) sulfuric acid was added, and the solution was immediately titrated with an 0.25 mmol·L−1 iodine solution to a blue endpoint (color stable for 20 seconds).
Linearity was determined by performing three replicates of calibration curves of high-concentration red, white and balsamic wine vinegar (19, 38, 75,150 mg/L) and low-concentration balsamic wine vinegar (1.88, 3.75, 7.50, 15 mg/L).
Method precision was evaluated by conducting ten analyses on the same sample and verifying normality by Shapiro-Wilk [21] and the anomalous data from the Huber test [22].
The LLOQ (signal/noise ratio ⩾ 10) and LLOD (lowest concentrations of SO2 that were detectable in all replicates but not necessarily quantified and distinguished from zero) defined method sensitivity. The LLOQ dilution factor gives the lower end of the measuring range.
Dilution factor=read concentration∗100weight∗ rate | (1) |
The upper end of the measuring range was given by the last point of the calibration curve line. Reproducibility and repeatability were detected to validate method precision:
Reproducibility=Standard deviation of analyzed samplesStandard deviation of reference samples | (2) |
Uncertainties (type A and B) were measured following as reported by Dini et al. [23,24] and the EURACHEM/CITAC guide [12].
Type A uncertainties were estimated from 10 repeated readings of the same sample.
UType A=√varianceDegrees of freedom | (3) |
Type B uncertainties considered were:
The uncertainties related to standard preparation (U(mr)); uncertainties related to the calibration curve (U(ct)), uncertainties related to balances (U(bt)), uncertainties related to accuracy (due to burette use) (U(m)), uncertainties related to accuracy (due to 50 mL pipette use) (U(p))
U(mr) was found from each standards' analysis certificate. U(ct) was appraised for standard at three concentrations in triplicate. U(bt) was decided considering a certificate of repeatability (0.000029 g), calibration (0.00060 g), and stability (0.000032 g). U(m) was evaluated from a certificate of calibration (0.1 mL) and repeatability (0.0010 mL). The U(p) was found from a certificate of calibration (0.028 mL), variation in volume based on temperature (0.0003 mL), and repeatability (0.001 mL). The method accuracy was found.
Accuracy=|−XOffic−−X|√S2r+U2Official≤tp | (4) |
|−XOffic = official method value
−Xx = media repeatability values
S2r = standard deviation2
U2Official = reference material uncertainty 2
The statistical analyses were performed by Statistica software Version 7.0 (StatSoft, Inc. USA).
In commodity laboratories, automated equipment often substitutes the official methods. The automated analyzers do not require specialized personnel, improve safety, reduce the analysis time, and decrease the cost of analyses. This work validated a colorimetric method, performed by an automated analyzer, to determine NaCl levels in canned tomatoes using the international guidelines ISO/IEC (2017) [25]. According to international guidelines, the primary validation process explains a method's operative limits and performance not adequately characterized. In this case, the validation process was necessary to establish the commercial test's validity when applied to the assay of SO2 in vinegar. The vinegar is a complex matrix, and the presence of interferents can negatively affect the results reliability. The objective was achieved by comparing the results obtained by the colorimetric method to those obtained by the Volhard test, considered a reference method (Ministerial Decree 03/02/1989–SO GU SG n 168 20/07/1989 Met 33). The parameters evaluated were working range (linearity range, LOQ, LOD, measuring range), recovery, precision, accuracy, and measurement uncertainty (associated with the analytical data). Statistical analyses were used to estimate validation qualities against fixed acceptance criteria.
The working range defines the impact of the sample preparation (i.e., dilutions) and the analytical procedure on the reliability of the results. The procedure's suitability for the intended use is confirmed by a linear relationship between analyte concentration and response.
The method linearity was evaluated by regression coefficient determination (Figure 1, Table S1). The ANOVA test estimated the distribution of residuals (procedure errors) across the calibration curve (Figure 2, Table S2).
The regression coefficient close to 1 of the calibration curve and the normal residual distribution evaluated by ANOVA confirmed the method's linearity.
The method detection limit was tested by repeated analysis of blank samples. LLOD and LLOQ were derived from the regression curve (Table 1).
LLOD (mg/L) | LLOQ (mg/L) | Measuring range (mg/L) | |
Low-concentration balsamic wine vinegar | 1.38 | 4.03 | 4.03 ≤ Measuring range ≤ 15 |
Low-concentration red and white and high concentration balsamic wine kinds of vinegar | 2.72 | 6.06 | 6.06 ≤ Measuring range ≤ 150 |
The test's measuring range, able to determine the concentrations of SO2 admissible in vinegar by law, demonstrated the method's selectivity.
Test precision serves to establish the effect of impurities on the dosage. Test precision was evaluated by estimating the repeatability and reproducibility of the test. The repeatability should be assessed by employing a minimum of 9 tests covering the range of the procedure. In this work, ten spectrophotometric analyses were carried out on the same sample of each type of vinegar to determine the repeatability of the two methods. The Shapiro-Wilk test was employed to prove the continuous variables' normal distribution, and the Huber test to evaluate the random errors (outliers) that deviate from a normal distribution.
The higher repeatability limit for the tested method than the Ripper test, the data normally distributed studied by Shapiro–Wilk, and the absence of outliers measured by the Huber test (Tables 2–6) demonstrated the compliance between the two methods.
Spectrophotometric method | ||
Sample | ![]() | |
1 | 15.440 | |
2 | 14.570 | |
3 | 15.260 | |
4 | 14.990 | |
5 | 15.270 | |
6 | 15.140 | |
7 | 13.930 | |
8 | 16.750 | |
9 | 14.680 | |
10 | 16.550 | |
Ripper Schmitt method | ||
Sample | ![]() | |
1 | 14.080 | |
2 | 15.360 | |
3 | 16.640 | |
4 | 15.360 | |
5 | 14.080 | |
6 | 16.640 | |
7 | 15.00 | |
8 | 12.00 | |
9 | 15.360 | |
10 | 19.00 |
Spectrophotometric method | ||
Sample | ![]() | |
1 | 57.02 | |
2 | 54.32 | |
3 | 50.42 | |
4 | 51.98 | |
5 | 54.25 | |
6 | 49.46 | |
7 | 53.17 | |
8 | 51.99 | |
9 | 57.05 | |
10 | 53.45 | |
Ripper Schmitt method | ||
Sample | ||
1 | 48.00 | ![]() |
2 | 46.08 | |
3 | 44.16 | |
4 | 42.88 | |
5 | 42.24 | |
6 | 44.80 | |
7 | 41.60 | |
8 | 44.16 | |
9 | 42.24 | |
10 | 47.36 |
Spectrophotometric method | ||
Sample | ![]() | |
1 | 21.02 | |
2 | 20.89 | |
3 | 22.07 | |
4 | 21.45 | |
5 | 22.25 | |
6 | 21.31 | |
7 | 22.14 | |
8 | 22.98 | |
9 | 23.05 | |
10 | 22.87 | |
Ripper Schmitt method | ||
Sample | ![]() | |
1 | 29.18 | |
2 | 28.67 | |
3 | 27.98 | |
4 | 28.03 | |
5 | 28.67 | |
6 | 28.67 | |
7 | 28.67 | |
8 | 29.70 | |
9 | 28.67 | |
10 | 29.55 |
Spectrophotometric method | ||
Sample | ![]() | |
1 | 90.79 | |
2 | 97.33 | |
3 | 98.96 | |
4 | 97.95 | |
5 | 99.15 | |
6 | 99.16 | |
7 | 96.36 | |
8 | 87.39 | |
9 | 90.38 | |
10 | 91.23 | |
Ripper Schmitt method | ||
Sample | ![]() | |
1 | 96.00 | |
2 | 99.20 | |
3 | 95.36 | |
4 | 97.92 | |
5 | 86.40 | |
6 | 86.40 | |
7 | 86.40 | |
8 | 84.48 | |
9 | 98.36 | |
10 | 94.25 |
Spectrophotometric method | ||
Sample | ![]() | |
1 | 14.00 | |
2 | 11.52 | |
3 | 16.44 | |
4 | 12.80 | |
5 | 16.60 | |
6 | 12.44 | |
7 | 14.20 | |
8 | 13.12 | |
9 | 11.55 | |
10 | 11.68 | |
Ripper Schmitt method | ||
Sample | ![]() | |
1 | 10.88 | |
2 | 9.60 | |
3 | 12.16 | |
4 | 10.24 | |
5 | 10.24 | |
6 | 8.96 | |
7 | 7.68 | |
8 | 9.65 | |
9 | 8.32 | |
10 | 8.32 |
The method reproducibility was reported in Table 7.
Sample | Reproducibility |
White wine vinegar with a low SO2 concentration | 0.8551.86= 0.460 |
White wine vinegar with a high SO2 concentration | 2.502.22= 1.13 |
Red wine vinegar with a low SO2 concentration | 0.8070.560= 1.43 |
Red wine vinegar with a high SO2 concentration | 4.445.59= 0.794 |
Balsamic wine vinegar with a low SO2 concentration | 1.881.35= 1.39 |
Depending on the degree of freedom (n=9) Upper limit of reproducibility = 0.548; Lower limit of reproducibility = 1.480 |
Accuracy measures the agreement of a measurement with a reference value. It was obtained by comparing the measured results with an expected value. In this work, accuracy was determined by making ten analyses with both methods (official and colorimetric). The relative deviation % was calculated to evaluate the error (Tables 8–11).
With low SO2 concentration | ||
![]() |
Student's test Hypothesized difference | 0 |
t statistic | 0.09 | |
DF | 9 | |
p-value | 0.9329 | |
With high SO2 concentration | ||
![]() |
Student's test Hypothesized difference | 0 |
t statistic | −9.10 | |
DF | 9 | |
p-value | < 0.0001 | |
y = concentration official meth/colorimetric method x = concentration official meth/colorimetric meth2 |
With low SO2 concentration | ||
![]() |
Student's test Hypothesized difference | 0 |
t statistic | −27.22 | |
DF | 9 | |
p-value | < 0.0001 | |
With high SO2 concentration | ||
![]() |
Student's test Hypothesized difference | 0 |
t statistic | −1.38 | |
DF | 9 | |
p-value | 0.2019 | |
y = concentration official method/colorimetric method x = concentration official method/colorimetric method2 |
With low SO2 concentration | ||
![]() |
Student's test Hypothesized difference | 0 |
t statistic | −8.04 | |
DF | 9 | |
p-value | < 0.0001 | |
y = concentration official method/colorimetric method x = concentration official method/colorimetric method2 |
Vinegar samples | Average deviation | Average of the averages | Relative deviation % |
White wine vinegar with a low SO2 concentration | 0.059 | 15.32 | 0.38 |
White wine vinegar with a high SO2 concentration | −8.959 | 48.83 | 18.34 |
Redwine vinegar with low SO2 concentration | 6.775 | 25.39 | 26.68 |
Red wine vinegar with a high SO2 concentration | −2.693 | 93.52 | 2.88 |
Balsamic wine vinegar with a low SO2 concentration | −4.283 | 11.29 | 37.93 |
Our results showed that the average deviation was very high in samples with low SO2 concentrations and decreased with high SO2 concentrations (Table 11), demonstrating the matrix independence and measurement dependence of the systematic errors.
The measurement uncertainties evaluate the errors associated with a measurement. They affect the accuracy and precision of the measurements. The uncertainties measure is recommended by the international standard ISO/IEC 17025:2017 [25,26,27]. It gives the analytical procedure quality and supports the interpretation of results [26]. The uncertainties are categorized as Type A if they are measured by the statistical analysis of reiterated measurements (linked to the spread of experimental data) and Type B if they are evaluated by other available information (i.e., instrument specifications, apparatus calibration, etc.) Standard deviation measurements confirmed the type A results' reliability for the number of degrees of freedom considered (Tables 12–16). Also, type B uncertainties were considered irrelevant since they were lower than those from the Ripper test.
Type A standard uncertainties | |||||
X | Spectrophotometric method | Ripper-Schmitt method | |||
1 | 15.44 | 14.08 | |||
2 | 14.57 | 15.36 | |||
3 | 15.26 | 16.64 | |||
4 | 14.99 | 15.36 | |||
5 | 15.27 | 14.08 | |||
6 | 15.14 | 16.64 | |||
7 | 13.93 | 15.00 | |||
8 | 17.10 | 12.00 | |||
9 | 14.68 | 15.36 | |||
10 | 16.55 | 19.00 | |||
Xm | 15.2930 | 15.3520 | |||
Y = 47x + 547 | |||||
Standard deviation | 0.9274 | 1.8625 | |||
Relative deviation (sr) | 0.0606 | 0.1213 | |||
Type A uncertainity = √varianceDegrees of freedom | 0.309 | 0.621 | |||
Type B systematic uncertainties | |||||
Spectrophotometric method | |||||
Type B uncertainty Xm | U(p) | U(mr) | U(ct) | U(bt) | U(m) |
7.500000 | 1.244771 | ||||
Type B uncertainty Xm/radq | 0.000000 | 2.165064 | 0.359334 | 0.000000 | 0.000000 |
Uncertainty u (Xm)B/Xm | 0.0000000 | 0.014434 | 0.054693 | 0.0000000 | 0.0000000 |
Resulting relative uncertainty u(y)/y | 0.08293 | ||||
Resulting uncertainty u(y) | 1.268 | ||||
Coverage factor k (2 < k < 3) | 2 | ||||
Extended uncertainty U(y) | 2.536 | ||||
Ripper -Schmitt method | U(p) | U(mr) | U(ct) | U(bt) | U(m) |
Type B uncertainty Xm | 0.050000 | 0.050000 | 0.030000 | ||
Type B uncertainty Xm/radq | 0.014434 | 0.014434 | 0.000000 | 0.000000 | 0.008660 |
Uncertainty u (Xm)B/Xm | 0.002887 | 0.000940 | 0.0000000 | 0.000087 | |
Resulting relative uncertainty u(y)/y | 0.12136 | ||||
Resulting uncertainty u(y) | 1.863 | ||||
Coverage factor k (2 < k < 3) | 2 | ||||
Extended uncertainty U(y) | 3.726 | ||||
U(p) uncertainties related to accuracy (due to 50 mL pipette use); U(mr) uncertainties related to standard preparation; U(ct) uncertainties related to the calibration curve; U(bt) uncertainties related to balances; U(m) uncertainties related to accuracy (due to burette use). |
Type A standard uncertainties | |||||
X | Spectrophotometric method | Ripper-Schmitt method | |||
1 | 57.02 | 48.00 | |||
2 | 54.32 | 46.08 | |||
3 | 50.42 | 44.16 | |||
4 | 51.98 | 42.88 | |||
5 | 54.25 | 42.24 | |||
6 | 49.46 | 44.80 | |||
7 | 53.17 | 41.60 | |||
8 | 51.99 | 44.16 | |||
9 | 57.05 | 42.24 | |||
10 | 53.45 | 47.36 | |||
Xm | 53.3110 | 44.3520 | |||
Y = 46.6x + 567 | |||||
Standard deviation | |||||
Relative deviation (sr) | |||||
Type B systematic uncertainties | |||||
Spectrophotometric method | |||||
Type B uncertainty Xm Type B uncertainty Xm/radq Uncertainty u (Xm)B/Xm |
U(p) | U(mr) | U(ct) | U(bt) | U(m) |
0.050000 | 0.417939 | ||||
0.000000 | 0.014434 | 0.120649 | 0.000000 | 0.000000 | |
0.0000000 | 0.000096 | 0.120649 | 0.0000000 | 0.0000000 | |
Resulting relative uncertainty u(y)/y | 0.12946 | ||||
Resulting uncertainty u(y) | 6.902 | ||||
Coverage factor k (2 < k < 3) | 2 | ||||
Extended uncertainty U(y) | 13.803 | ||||
Ripper -Schmitt method | |||||
U(p) | U(mr) | U(ct) | U(bt) | U(m) | |
Type B uncertainty Xm | 0.050000 | 0.050000 | 0.030000 | ||
Type B uncertainty Xm/radq | |||||
Uncertainty u (Xm)B/Xm | 0.014434 | 0.014434 | 0.000000 | 0.000000 | 0.008660 |
0.002887 | 0.000325 | 0.0000000 | 0.000087 | ||
Resulting relative uncertainty u(y)/y | 0.05009 | ||||
Resulting uncertainty u(y) | 2.222 | ||||
Coverage factor k (2 < k < 3) | 2 | ||||
Extended uncertainty U(y) | 4.444 | ||||
U(p) uncertainties related to accuracy (due to 50 mL pipette use); U(mr) uncertainties related to standard preparation; U(ct) uncertainties related to the calibration curve; U(bt) uncertainties related to balances; U(m) uncertainties related to accuracy (due to burette use). |
Type A standard uncertainties | |||||
X | Spectrophotometric method | Ripper-Schmitt method | |||
1 | 21.02 | 29.18 | |||
2 | 20.89 | 28.67 | |||
3 | 22.07 | 27.98 | |||
4 | 21.45 | 28.03 | |||
5 | 22.25 | 28.67 | |||
6 | 21.31 | 28.67 | |||
7 | 22.14 | 28.67 | |||
8 | 22.98 | 29.70 | |||
9 | 23.05 | 28.67 | |||
10 | 22.87 | 29.55 | |||
Xm | 22.0040 | 28.7790 | |||
Y = 47x + 547 | |||||
Standard deviation | 0.8072 | 0.5639 | |||
Relative deviation (sr) | 0.367 | 0.0196 | |||
Type A uncertainty y = √varianceDegrees of freedom | 0.269 | 0.188 | |||
Type B systematic uncertainties | |||||
Spectrophotometric method | |||||
Type B uncertainty Xm Type B uncertainty Xm/radq Uncertainty u (Xm)B/Xm | U(p) | U(mr) | U(ct) | U(bt) | U(m) |
0.050000 | 0.417939 | ||||
0.000000 | 0.014434 | 0.120649 | 0.000000 | 0.000000 | |
0.0000000 | 0.000096 | 0.120649 | 0.0000000 | 0.0000000 | |
Resulting relative uncertainty u(y)/y | 0.12610 | ||||
Resulting uncertainty u(y) | 2.775 | ||||
Coverage factor k (2 < k < 3) | 2 | ||||
Extended uncertainty U(y) | 5.550 | ||||
Ripper -Schmitt method | |||||
Type B uncertainty Xm Type B uncertainty Xm/radq Uncertainty u (Xm)B/Xm | U(p) | U(mr) | U(ct) | U(bt) | U(m) |
0.050000 | 0.050000 | 0.030000 | |||
0.014434 | 0.014434 | 0.000000 | 0.000000 | 0.008660 | |
0.002887 | 0.000502 | 0.0000000 | 0.000087 | ||
Resulting relative uncertainty u(y)/y | 0.01981 | ||||
Resulting uncertainty u(y) | 0.570 | ||||
Coverage factor k (2 < k < 3) | 2 | ||||
Extended uncertainty U(y) | 1.140 | ||||
U(p) uncertainties related to accuracy (due to 50 mL pipette use); U(mr) uncertainties related to standard preparation; U(ct) uncertainties related to the calibration curve; U(bt) uncertainties related to balances; U(m) uncertainties related to accuracy (due to burette use). |
Type A standard uncertainties | |||||
X | Spectrophotometric method | Ripper-Schmitt method | |||
1 | 90.79 | 96.00 | |||
2 | 97.33 | 99.20 | |||
3 | 98.96 | 95.36 | |||
4 | 97.95 | 97.92 | |||
5 | 99.15 | 86.40 | |||
6 | 99.16 | 86.40 | |||
7 | 96.36 | 86.40 | |||
8 | 87.39 | 84.48 | |||
9 | 90.38 | 95.36 | |||
10 | 91.23 | 94.25 | |||
Xm | 94.8700 | 92.1770 | |||
Y = 46.6x + 567 | |||||
Standard deviation | 4.4374 | 5.5861 | |||
Relative deviation (sr) | 0.0468 | 0..0606 | |||
Type A uncertainty y = √varianceDegrees of freedom | 1.479 | 1.862 | |||
Type B systematic uncertainties | |||||
Spectrophotometric method | |||||
Type B uncertainty Xm Type B uncertainty Xm/radq Uncertainty u (Xm)B/Xm | U(p) | U(mr) | U(ct) | U(bt) | U(m) |
0.050000 | 0.417939 | ||||
0.000000 | 0.014434 | 0.120649 | 0.000000 | 0.000000 | |
0.0000000 | 0.00096 | 0.120649 | 0.0000000 | 0.0000000 | |
Resulting relative uncertainty u(y)/y | 0.12940 | ||||
Resulting uncertainty u(y) | 12.276 | ||||
Coverage factor k (2 < k < 3) | 2 | ||||
Extended uncertainty U(y) | 24.552 | ||||
Ripper -Schmitt method | |||||
Type B uncertainty Xm Type B uncertainty Xm/radq Uncertainty u (Xm)B/Xm | U(p) | U(mr) | U(ct) | U(bt) | U(m) |
0.050000 | 0.050000 | 0.030000 | |||
0.014434 | 0.014434 | 0.000000 | 0.000000 | 0.008660 | |
0.002887 | 0.000157 | 0.0000000 | 0.000087 | ||
Resulting relative uncertainty u(y)/y | 0.06067 | ||||
Resulting uncertainty u(y) | 5.593 | ||||
Coverage factor k (2 < k < 3) | 2 | ||||
Extended uncertainty U(y) | 11.185 | ||||
U(p) uncertainties related to accuracy (due to 50 mL pipette use); U(mr) uncertainties related to standard preparation; U(ct) uncertainties related to the calibration curve; U(bt) uncertainties related to balances; U(m) uncertainties related to accuracy (due to burette use). |
Type A standard uncertainties | |||||
X | Spectrophotometric method | Ripper-Schmitt method | |||
1 | 14.00 | 10.88 | |||
2 | 11.52 | 9.60 | |||
3 | 16.44 | 12.16 | |||
4 | 12.80 | 10.24 | |||
5 | 16.60 | 10.24 | |||
6 | 12.44 | 8.96 | |||
7 | 14.20 | 7.68 | |||
8 | 13.12 | 5.12 | |||
9 | 11.55 | 8.32 | |||
10 | 11.68 | 8.32 | |||
Xm | 13.4350 | 9.1520 | |||
Y = 77.4x + 552 | |||||
Standard deviation | 1.8781 | 1.9564 | |||
Relative deviation (sr) | 0.1398 | 0.2138 | |||
Type A uncertainty y = √varianceDegrees of freedom | 0.626 | 0.652 | |||
Type B systematic uncertainties | |||||
Spectrophotometric method | |||||
Type B uncertainty Xm Type B uncertainty Xm/radq Uncertainty u (Xm)B/Xm |
U(p) | U(mr) | U(ct) | U(bt) | U(m) |
0.050000 | 0.000000 | ||||
0.000000 | 0.014434 | 0.000000 | 0.000000 | 0.000000 | |
0.0000000 | 0.000096 | 0.0000000 | 0.0000000 | 0.0000000 | |
Resulting relative uncertainty u(y)/y | 0.13979 | ||||
Resulting uncertainty u(y) | 1.878 | ||||
Coverage factor k (2 < k < 3) | 2 | ||||
Extended uncertainty U(y) | 3.756 | ||||
Ripper -Schmitt method | |||||
Type B uncertainty Xm Type B uncertainty Xm/radq Uncertainty u (Xm)B/Xm | U(p) | U(mr) | U(ct) | U(bt) | U(m) |
0.050000 | 0.050000 | 0.030000 | |||
0.014434 | 0.014434 | 0.000000 | 0.000000 | 0.008660 | |
0.002887 | 0.001577 | 0.0000000 | 0.000087 | ||
Resulting relative uncertainty u(y)/y | 0.21379 | ||||
Resulting uncertainty u(y) | 1.957 | ||||
Coverage factor k (2 < k < 3) | 2 | ||||
U(p) uncertainties related to accuracy (due to 50 mL pipette use); U(mr) uncertainties related to standard preparation; U(ct) uncertainties related to the calibration curve; U(bt) uncertainties related to balances; U(m) uncertainties related to accuracy (due to burette use). |
An automated colorimetric method was validated to determine the SO2 concentration in vinegar samples, as it could be helpful in the laboratory routine to reduce the analysis time, use of specialized personnel, and analysis costs. The validation was obtained by comparing the colorimetric test with the "Ripper test" (reference test for European legislation).
The test measuring range, sensitivity, and precision complied with those obtained using the Ripper method. The accuracy parameter was not respected in samples containing low dosages of SO2. The type A and B uncertainties of the rapid analytical method tested were lower than the Ripper method uncertainties. Therefore, this method can be considered reliable for determining SO2 only in vinegar with a high concentration of SO2. New studies must be performed to optimize method performance if it is to be used to determine low SO2 levels in vinegar.
The authors declare no conflict of interest.
Standard | X (g/L) | Yfound | Ycalculated | |
High and low concentration red and white and high concentration balsamic wine vinegars (mg/L) | 1 | 19 | 1451.6008 | 1450 |
2 | 38 | 2319.5044 | 2340 | |
3 | 75 | 4022.3055 | 4060 | |
4 | 150 | 7598.7301 | 7560 | |
1 | 19 | 1483.0711 | 1450 | |
2 | 38 | 2323.9193 | 2340 | |
3 | 75 | 4057.2913 | 4060 | |
4 | 150 | 7559.4303 | 7560 | |
1 | 19 | 1474.3658 | 1450 | |
2 | 38 | 2361.0670 | 2340 | |
3 | 75 | 4037.2093 | 4060 | |
4 | 150 | 7549.0236 | 7560 | |
Low concentration balsamic wine vinegar (mg/L) | 1 | 1.88 | 708.8872 | 698 |
2 | 3.75 | 845.173 | 843 | |
3 | 7.5 | 1100.3899 | 1130 | |
4 | 15 | 1726.0638 | 1710 | |
1 | 1.88 | 717.8597 | 698 | |
2 | 3.75 | 846.4987 | 843 | |
3 | 7.5 | 1100.8734 | 1130 | |
4 | 15 | 1726.8831 | 1710 | |
1 | 1.88 | 717.468 | 698 | |
2 | 3.75 | 841.4904 | 843 | |
3 | 7.5 | 1100.2545 | 1130 | |
4 | 15 | 1728.1739 | 1710 |
High and low concentration red and white and high concentration balsamic wine vinegars | |||||
x | hi leverages coefficient |
ei absolute residuals |
eNi normalized residuals |
eSi studentized residuals |
eji standardized residuals |
19.0 | 0.171 | −1.31 | −0.051 | −0.056 | −0.054 |
38.0 | 0.118 | −18.9 | −0.742 | −0.790 | −0.774 |
75.0 | 0.084 | 40.5 | −1.590 | −1.661 | −1.852 |
150 | 0.293 | 40.4 | 1.584 | 1.884 | 2.226 |
19.0 | 0.171 | 30.2 | 1.182 | 1.299 | 1.351 |
38.0 | 0.118 | −14.5 | −0.569 | −0.606 | −0.585 |
75.0 | 0.084 | −5.56 | −0.218 | −0.228 | −0.217 |
150 | 0.293 | 1.12 | 0.044 | 0.052 | 0.049 |
19.0 | 0.171 | 21.5 | 0.841 | 0.924 | 0.917 |
38.0 | 0.118 | 22.6 | 0.888 | 0.945 | 0.90 |
75.0 | 0.084 | −25.6 | −1.005 | −1.050 | −1.057 |
150 | 0.293 | −9.29 | −0.364 | −0.433 | −0.415 |
Low concentration balsamic wine vinegar | |||||
x | hi leverages coefficient |
ei absolute residuals |
eNi normalized residuals |
eSi studentized residuals |
eji standardized residuals |
1.88 | 0.171 | 11.1 | 0.515 | 0.565 | 0.545 |
3.75 | 0.119 | 2.61 | 0.121 | 0.129 | 0.123 |
750 | 0.084 | −32.5 | −1.509 | −1.576 | −1.725 |
15.0 | 0.293 | 12.6 | 0.5186 | 0.697 | 0.678 |
1.88 | 0.171 | 20.1 | 0.932 | 1.023 | 1.026 |
3.75 | 0.119 | 3.93 | 0.183 | 0.195 | 0.185 |
7.50 | 0.84 | −32.0 | −1.486 | −1.553 | −1.691 |
15.0 | 0.293 | 13.4 | 0.625 | 0.743 | 0.725 |
1.88 | 0.171 | 19.7 | 0.914 | 1.003 | 1.004 |
3.75 | 0.119 | −1.08 | −0.050 | −0.053 | −0.050 |
7.50 | 0.084 | −32.6 | −1.515 | −1.583 | −1.735 |
15.0 | 0.293 | 14.7 | 0.685 | 0.814 | 0.799 |
[1] |
Abaje IB, Bello Y, Ahmad SA (2020) A review of air quality and concentrations of air pollutants in Nigeria. JASEM 24: 373–379. https://doi.org/10.4314/jasem.v24i2.25 doi: 10.4314/jasem.v24i2.25
![]() |
[2] | Schroeter LC (1966) Sulfur dioxide: Applications in foods, beverages, and pharmaceuticals, Long Island City: Pergamon Press, Inc. |
[3] |
Mandrile L, Cagnasso I, Berta L, et al. (2020) Direct quantification of sulfur dioxide in wine by Surface Enhanced Raman Spectroscopy. Food Chem 326: 127009. https://doi.org/10.1016/j.foodchem.2020.127009 doi: 10.1016/j.foodchem.2020.127009
![]() |
[4] |
Morgan SC, Tantikachornkiat M, Scholl CM, et al. (2019) The effect of sulfur dioxide addition at crush on the fungal and bacterial communities and the sensory attributes of Pinot gris wines. Int J Food Microbiol 290: 1–14. https://doi.org/10.1016/j.ijfoodmicro.2018.09.020 doi: 10.1016/j.ijfoodmicro.2018.09.020
![]() |
[5] |
Gabriele M, Gerardi C, Lucejko JJ, et al. (2018) Effects of low sulfur dioxide concentrations on bioactive compounds and antioxidant properties of Aglianico red wine. Food Chem 245: 1105–1112. https://doi.org/10.1016/j.foodchem.2017.11.060 doi: 10.1016/j.foodchem.2017.11.060
![]() |
[6] | Giacosa S, Segade SR, Cagnasso E, et al. (2019) Red Wine Technology, 1 Eds., New York: Academic Press, 309–321. https://doi.org/10.1016/B978-0-12-814399-5.00021-9 |
[7] |
Seralini G, Douzelet J, Halley J (2021) Sulfur in Wines and Vineyards: Taste and Comparative Toxicity to Pesticides. Food Nutr J 6: 231. https://doi.org/10.29011/2575-7091.100131 doi: 10.29011/2575-7091.100131
![]() |
[8] | Directive 2003/89/EC of the European Parliament and of the Council of 10 November 2003 amending Directive 2000/13/EC as regards indication of the ingredients present in foodstuffs (Text with EEA relevance). (2003) Available from: https://eur-lex.europa.eu/legal-content/en/ALL/?uri=CELEX%3A32003L008913/12/2014. |
[9] | Commission Regulation No 606/2009 of 10 July 2009. Laying down certain detailed rules for implementing Council Regulation (EC) No 479/2008 as regards the categories of grapevine products, oenological practices and the applicable restrictions. (2009) Available from: https://eur-lex.europa.eu/eli/reg/2009/606/oj/eng. |
[10] |
Jenkins TW, Howe PA, Sacks GL, et al. (2020) Determination of molecular and " Truly" free sulfur dioxide in wine: a comparison of headspace and conventional methods. AJEV 71: 222–230. https://doi.org/10.5344/ajev.2020.19052 doi: 10.5344/ajev.2020.19052
![]() |
[11] |
Danchana K, Clavijo S, Cerdà V (2019) Conductometric determination of sulfur dioxide in wine using a multipumping system coupled to a gas-diffusion cell. Anal Lett 52: 1363–1378. https://doi.org/10.1080/00032719.2018.1539742 doi: 10.1080/00032719.2018.1539742
![]() |
[12] | Eurachem Guide (2014) Eurachem Guide: The Fitness for Purpose of Analytical Methods—A Laboratory Guide to Method Validation and Related Topics, 2 Eds., Available from: https://www.eurachem.org/images/stories/Guides/pdf/MV_guide_2nd_ed_EN.pdf. |
[13] |
Pepe P, Bosco A, Capuano F, et al. (2021) Towards an Integrated Approach for Monitoring Toxoplasmosis in Southern Italy. Animals 1: 1949. https://doi.org/10.3390/ani11071949 doi: 10.3390/ani11071949
![]() |
[14] |
Laneri S, Di Lorenzo R, Sacchi A, et al. (2019) Dosage of bioactive molecules in the nutricosmeceutical helix aspersa muller mucus and formulation of new cosmetic cream with moisturizing effect. Nat Prod Commun 14: 1–7. https://doi.org/10.1177/1934578X19868606 doi: 10.1177/1934578X19868606
![]() |
[15] |
Mancusi F, Capuano S, Girardi O, et al. (2022) Detection of SARS-CoV-2 RNA in bivalve mollusks by droplet digital RT-PCR (dd RT-PCR). Int J Environ Res Public Health 19: 943. https://doi.org/10.3390/ijerph19020943 doi: 10.3390/ijerph19020943
![]() |
[16] |
Fanelli F, Cozzi G, Raiola A, et al. (2017) Raisins and currants as conventional nutraceuticals in Italian market: Natural occurrence of Ochratoxin A. J Food Sci 82: 2306–2312. https://doi: 10.1111/1750-3841.13854 doi: 10.1111/1750-3841.13854
![]() |
[17] |
Mancusi A, Giordano A, Bosco A, et al. (2022) Development of a droplet digital polymerase chain reaction tool for the detection of Toxoplasma gondii in meat samples. Parasitol Res 121: 1467–1473. https://doi.org/10.1007/s00436-022-07477-9 doi: 10.1007/s00436-022-07477-9
![]() |
[18] |
Capuano F, Capparelli R, Mancusi A, et al. (2013) Detection of Brucella spp. in Stretched Curd Cheese as Assessed by Molecular Assays. J Food Saf 33: 145–148. https://doi.org/10.1111/jfs.12034 doi: 10.1111/jfs.12034
![]() |
[19] |
Cristiano D, Peruzy MF, Aponte M, et al. (2021) Comparison of droplet digital PCR vs real-time PCR for Yersinia enterocolitica detection in vegetables. Int J Food Microbiol 354: 109321. https://doi.org/10.1016/j.ijfoodmicro.2021.109321 doi: 10.1016/j.ijfoodmicro.2021.109321
![]() |
[20] | OIV 2007 Organisation Internationale de la Vigne et du Vin (2007) Available from: https://www.oiv.int/en/technical-standards-and-documents/methods-of-analysis/compendium-of-international-methods-of-analysis-of-wines-and-musts. |
[21] |
Shapiro SS, Wilk MB (1965) An analysis of variance test for normality (complete samples). Biometrika 52: 591–611. https://doi.org/10.2307/2333709 doi: 10.2307/2333709
![]() |
[22] | Huber PJ (1981) Robust statistics, New York: Wiley, 1–384. |
[23] |
Dini I, Seccia S, Senatore A, et al. (2019) Development and validation of an analytical method for total polyphenols quantification in extra virgin olive oils. Food Anal Met 13: 457–464. https://doi.org/10.1007/s12161-019-01657-7 doi: 10.1007/s12161-019-01657-7
![]() |
[24] |
Dini I, Di Lorenzo R, Senatore A, et al. (2020) Validation of rapid enzymatic quantification of acetic acid in vinegar on automated spectrophotometric system. Foods 9: 761. https://doi.org/10.3390/foods9060761 doi: 10.3390/foods9060761
![]() |
[25] | ISO/IEC 17025: 2017 General requirements for the competence of testing and calibration laboratories (2017) Available from: https://www.iso.org/standard/66912.html. |
[26] |
Molognoni L, Daguer H, Dos Santos IR, et al. (2019) Influence of method validation parameters in the measurement uncertainty estimation by experimental approaches in food preservatives analysis. Food Chem 282: 147–152. https://doi.org/10.1016/j.foodchem.2018.12.115 doi: 10.1016/j.foodchem.2018.12.115
![]() |
[27] |
Dini I, Di Lorenzo R, Senatore A, et al. (2021) Comparison between Mid-Infrared (ATR-FTIR) spectroscopy and official analysis methods for determination of the concentrations of alcohol, SO2, and total acids in wine. Separations 8: 191. https://doi.org/10.3390/separations8100191 doi: 10.3390/separations8100191
![]() |
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2. | Irene Dini, Dario Tuccillo, Daniele Coppola, Margherita-Gabriella De Biasi, Elena Morelli, Andrea Mancusi, Validation of an Eco-Friendly Automated Method for the Determination of Glucose and Fructose in Wines, 2023, 28, 1420-3049, 5585, 10.3390/molecules28145585 |
LLOD (mg/L) | LLOQ (mg/L) | Measuring range (mg/L) | |
Low-concentration balsamic wine vinegar | 1.38 | 4.03 | 4.03 ≤ Measuring range ≤ 15 |
Low-concentration red and white and high concentration balsamic wine kinds of vinegar | 2.72 | 6.06 | 6.06 ≤ Measuring range ≤ 150 |
Spectrophotometric method | ||
Sample | ![]() | |
1 | 15.440 | |
2 | 14.570 | |
3 | 15.260 | |
4 | 14.990 | |
5 | 15.270 | |
6 | 15.140 | |
7 | 13.930 | |
8 | 16.750 | |
9 | 14.680 | |
10 | 16.550 | |
Ripper Schmitt method | ||
Sample | ![]() | |
1 | 14.080 | |
2 | 15.360 | |
3 | 16.640 | |
4 | 15.360 | |
5 | 14.080 | |
6 | 16.640 | |
7 | 15.00 | |
8 | 12.00 | |
9 | 15.360 | |
10 | 19.00 |
Spectrophotometric method | ||
Sample | ![]() | |
1 | 57.02 | |
2 | 54.32 | |
3 | 50.42 | |
4 | 51.98 | |
5 | 54.25 | |
6 | 49.46 | |
7 | 53.17 | |
8 | 51.99 | |
9 | 57.05 | |
10 | 53.45 | |
Ripper Schmitt method | ||
Sample | ||
1 | 48.00 | ![]() |
2 | 46.08 | |
3 | 44.16 | |
4 | 42.88 | |
5 | 42.24 | |
6 | 44.80 | |
7 | 41.60 | |
8 | 44.16 | |
9 | 42.24 | |
10 | 47.36 |
Spectrophotometric method | ||
Sample | ![]() | |
1 | 21.02 | |
2 | 20.89 | |
3 | 22.07 | |
4 | 21.45 | |
5 | 22.25 | |
6 | 21.31 | |
7 | 22.14 | |
8 | 22.98 | |
9 | 23.05 | |
10 | 22.87 | |
Ripper Schmitt method | ||
Sample | ![]() | |
1 | 29.18 | |
2 | 28.67 | |
3 | 27.98 | |
4 | 28.03 | |
5 | 28.67 | |
6 | 28.67 | |
7 | 28.67 | |
8 | 29.70 | |
9 | 28.67 | |
10 | 29.55 |
Spectrophotometric method | ||
Sample | ![]() | |
1 | 90.79 | |
2 | 97.33 | |
3 | 98.96 | |
4 | 97.95 | |
5 | 99.15 | |
6 | 99.16 | |
7 | 96.36 | |
8 | 87.39 | |
9 | 90.38 | |
10 | 91.23 | |
Ripper Schmitt method | ||
Sample | ![]() | |
1 | 96.00 | |
2 | 99.20 | |
3 | 95.36 | |
4 | 97.92 | |
5 | 86.40 | |
6 | 86.40 | |
7 | 86.40 | |
8 | 84.48 | |
9 | 98.36 | |
10 | 94.25 |
Spectrophotometric method | ||
Sample | ![]() | |
1 | 14.00 | |
2 | 11.52 | |
3 | 16.44 | |
4 | 12.80 | |
5 | 16.60 | |
6 | 12.44 | |
7 | 14.20 | |
8 | 13.12 | |
9 | 11.55 | |
10 | 11.68 | |
Ripper Schmitt method | ||
Sample | ![]() | |
1 | 10.88 | |
2 | 9.60 | |
3 | 12.16 | |
4 | 10.24 | |
5 | 10.24 | |
6 | 8.96 | |
7 | 7.68 | |
8 | 9.65 | |
9 | 8.32 | |
10 | 8.32 |
Sample | Reproducibility |
White wine vinegar with a low SO2 concentration | 0.8551.86= 0.460 |
White wine vinegar with a high SO2 concentration | 2.502.22= 1.13 |
Red wine vinegar with a low SO2 concentration | 0.8070.560= 1.43 |
Red wine vinegar with a high SO2 concentration | 4.445.59= 0.794 |
Balsamic wine vinegar with a low SO2 concentration | 1.881.35= 1.39 |
Depending on the degree of freedom (n=9) Upper limit of reproducibility = 0.548; Lower limit of reproducibility = 1.480 |
With low SO2 concentration | ||
![]() |
Student's test Hypothesized difference | 0 |
t statistic | 0.09 | |
DF | 9 | |
p-value | 0.9329 | |
With high SO2 concentration | ||
![]() |
Student's test Hypothesized difference | 0 |
t statistic | −9.10 | |
DF | 9 | |
p-value | < 0.0001 | |
y = concentration official meth/colorimetric method x = concentration official meth/colorimetric meth2 |
With low SO2 concentration | ||
![]() |
Student's test Hypothesized difference | 0 |
t statistic | −27.22 | |
DF | 9 | |
p-value | < 0.0001 | |
With high SO2 concentration | ||
![]() |
Student's test Hypothesized difference | 0 |
t statistic | −1.38 | |
DF | 9 | |
p-value | 0.2019 | |
y = concentration official method/colorimetric method x = concentration official method/colorimetric method2 |
With low SO2 concentration | ||
![]() |
Student's test Hypothesized difference | 0 |
t statistic | −8.04 | |
DF | 9 | |
p-value | < 0.0001 | |
y = concentration official method/colorimetric method x = concentration official method/colorimetric method2 |
Vinegar samples | Average deviation | Average of the averages | Relative deviation % |
White wine vinegar with a low SO2 concentration | 0.059 | 15.32 | 0.38 |
White wine vinegar with a high SO2 concentration | −8.959 | 48.83 | 18.34 |
Redwine vinegar with low SO2 concentration | 6.775 | 25.39 | 26.68 |
Red wine vinegar with a high SO2 concentration | −2.693 | 93.52 | 2.88 |
Balsamic wine vinegar with a low SO2 concentration | −4.283 | 11.29 | 37.93 |
Type A standard uncertainties | |||||
X | Spectrophotometric method | Ripper-Schmitt method | |||
1 | 15.44 | 14.08 | |||
2 | 14.57 | 15.36 | |||
3 | 15.26 | 16.64 | |||
4 | 14.99 | 15.36 | |||
5 | 15.27 | 14.08 | |||
6 | 15.14 | 16.64 | |||
7 | 13.93 | 15.00 | |||
8 | 17.10 | 12.00 | |||
9 | 14.68 | 15.36 | |||
10 | 16.55 | 19.00 | |||
Xm | 15.2930 | 15.3520 | |||
Y = 47x + 547 | |||||
Standard deviation | 0.9274 | 1.8625 | |||
Relative deviation (sr) | 0.0606 | 0.1213 | |||
Type A uncertainity = √varianceDegrees of freedom | 0.309 | 0.621 | |||
Type B systematic uncertainties | |||||
Spectrophotometric method | |||||
Type B uncertainty Xm | U(p) | U(mr) | U(ct) | U(bt) | U(m) |
7.500000 | 1.244771 | ||||
Type B uncertainty Xm/radq | 0.000000 | 2.165064 | 0.359334 | 0.000000 | 0.000000 |
Uncertainty u (Xm)B/Xm | 0.0000000 | 0.014434 | 0.054693 | 0.0000000 | 0.0000000 |
Resulting relative uncertainty u(y)/y | 0.08293 | ||||
Resulting uncertainty u(y) | 1.268 | ||||
Coverage factor k (2 < k < 3) | 2 | ||||
Extended uncertainty U(y) | 2.536 | ||||
Ripper -Schmitt method | U(p) | U(mr) | U(ct) | U(bt) | U(m) |
Type B uncertainty Xm | 0.050000 | 0.050000 | 0.030000 | ||
Type B uncertainty Xm/radq | 0.014434 | 0.014434 | 0.000000 | 0.000000 | 0.008660 |
Uncertainty u (Xm)B/Xm | 0.002887 | 0.000940 | 0.0000000 | 0.000087 | |
Resulting relative uncertainty u(y)/y | 0.12136 | ||||
Resulting uncertainty u(y) | 1.863 | ||||
Coverage factor k (2 < k < 3) | 2 | ||||
Extended uncertainty U(y) | 3.726 | ||||
U(p) uncertainties related to accuracy (due to 50 mL pipette use); U(mr) uncertainties related to standard preparation; U(ct) uncertainties related to the calibration curve; U(bt) uncertainties related to balances; U(m) uncertainties related to accuracy (due to burette use). |
Type A standard uncertainties | |||||
X | Spectrophotometric method | Ripper-Schmitt method | |||
1 | 57.02 | 48.00 | |||
2 | 54.32 | 46.08 | |||
3 | 50.42 | 44.16 | |||
4 | 51.98 | 42.88 | |||
5 | 54.25 | 42.24 | |||
6 | 49.46 | 44.80 | |||
7 | 53.17 | 41.60 | |||
8 | 51.99 | 44.16 | |||
9 | 57.05 | 42.24 | |||
10 | 53.45 | 47.36 | |||
Xm | 53.3110 | 44.3520 | |||
Y = 46.6x + 567 | |||||
Standard deviation | |||||
Relative deviation (sr) | |||||
Type B systematic uncertainties | |||||
Spectrophotometric method | |||||
Type B uncertainty Xm Type B uncertainty Xm/radq Uncertainty u (Xm)B/Xm |
U(p) | U(mr) | U(ct) | U(bt) | U(m) |
0.050000 | 0.417939 | ||||
0.000000 | 0.014434 | 0.120649 | 0.000000 | 0.000000 | |
0.0000000 | 0.000096 | 0.120649 | 0.0000000 | 0.0000000 | |
Resulting relative uncertainty u(y)/y | 0.12946 | ||||
Resulting uncertainty u(y) | 6.902 | ||||
Coverage factor k (2 < k < 3) | 2 | ||||
Extended uncertainty U(y) | 13.803 | ||||
Ripper -Schmitt method | |||||
U(p) | U(mr) | U(ct) | U(bt) | U(m) | |
Type B uncertainty Xm | 0.050000 | 0.050000 | 0.030000 | ||
Type B uncertainty Xm/radq | |||||
Uncertainty u (Xm)B/Xm | 0.014434 | 0.014434 | 0.000000 | 0.000000 | 0.008660 |
0.002887 | 0.000325 | 0.0000000 | 0.000087 | ||
Resulting relative uncertainty u(y)/y | 0.05009 | ||||
Resulting uncertainty u(y) | 2.222 | ||||
Coverage factor k (2 < k < 3) | 2 | ||||
Extended uncertainty U(y) | 4.444 | ||||
U(p) uncertainties related to accuracy (due to 50 mL pipette use); U(mr) uncertainties related to standard preparation; U(ct) uncertainties related to the calibration curve; U(bt) uncertainties related to balances; U(m) uncertainties related to accuracy (due to burette use). |
Type A standard uncertainties | |||||
X | Spectrophotometric method | Ripper-Schmitt method | |||
1 | 21.02 | 29.18 | |||
2 | 20.89 | 28.67 | |||
3 | 22.07 | 27.98 | |||
4 | 21.45 | 28.03 | |||
5 | 22.25 | 28.67 | |||
6 | 21.31 | 28.67 | |||
7 | 22.14 | 28.67 | |||
8 | 22.98 | 29.70 | |||
9 | 23.05 | 28.67 | |||
10 | 22.87 | 29.55 | |||
Xm | 22.0040 | 28.7790 | |||
Y = 47x + 547 | |||||
Standard deviation | 0.8072 | 0.5639 | |||
Relative deviation (sr) | 0.367 | 0.0196 | |||
Type A uncertainty y = √varianceDegrees of freedom | 0.269 | 0.188 | |||
Type B systematic uncertainties | |||||
Spectrophotometric method | |||||
Type B uncertainty Xm Type B uncertainty Xm/radq Uncertainty u (Xm)B/Xm | U(p) | U(mr) | U(ct) | U(bt) | U(m) |
0.050000 | 0.417939 | ||||
0.000000 | 0.014434 | 0.120649 | 0.000000 | 0.000000 | |
0.0000000 | 0.000096 | 0.120649 | 0.0000000 | 0.0000000 | |
Resulting relative uncertainty u(y)/y | 0.12610 | ||||
Resulting uncertainty u(y) | 2.775 | ||||
Coverage factor k (2 < k < 3) | 2 | ||||
Extended uncertainty U(y) | 5.550 | ||||
Ripper -Schmitt method | |||||
Type B uncertainty Xm Type B uncertainty Xm/radq Uncertainty u (Xm)B/Xm | U(p) | U(mr) | U(ct) | U(bt) | U(m) |
0.050000 | 0.050000 | 0.030000 | |||
0.014434 | 0.014434 | 0.000000 | 0.000000 | 0.008660 | |
0.002887 | 0.000502 | 0.0000000 | 0.000087 | ||
Resulting relative uncertainty u(y)/y | 0.01981 | ||||
Resulting uncertainty u(y) | 0.570 | ||||
Coverage factor k (2 < k < 3) | 2 | ||||
Extended uncertainty U(y) | 1.140 | ||||
U(p) uncertainties related to accuracy (due to 50 mL pipette use); U(mr) uncertainties related to standard preparation; U(ct) uncertainties related to the calibration curve; U(bt) uncertainties related to balances; U(m) uncertainties related to accuracy (due to burette use). |
Type A standard uncertainties | |||||
X | Spectrophotometric method | Ripper-Schmitt method | |||
1 | 90.79 | 96.00 | |||
2 | 97.33 | 99.20 | |||
3 | 98.96 | 95.36 | |||
4 | 97.95 | 97.92 | |||
5 | 99.15 | 86.40 | |||
6 | 99.16 | 86.40 | |||
7 | 96.36 | 86.40 | |||
8 | 87.39 | 84.48 | |||
9 | 90.38 | 95.36 | |||
10 | 91.23 | 94.25 | |||
Xm | 94.8700 | 92.1770 | |||
Y = 46.6x + 567 | |||||
Standard deviation | 4.4374 | 5.5861 | |||
Relative deviation (sr) | 0.0468 | 0..0606 | |||
Type A uncertainty y = √varianceDegrees of freedom | 1.479 | 1.862 | |||
Type B systematic uncertainties | |||||
Spectrophotometric method | |||||
Type B uncertainty Xm Type B uncertainty Xm/radq Uncertainty u (Xm)B/Xm | U(p) | U(mr) | U(ct) | U(bt) | U(m) |
0.050000 | 0.417939 | ||||
0.000000 | 0.014434 | 0.120649 | 0.000000 | 0.000000 | |
0.0000000 | 0.00096 | 0.120649 | 0.0000000 | 0.0000000 | |
Resulting relative uncertainty u(y)/y | 0.12940 | ||||
Resulting uncertainty u(y) | 12.276 | ||||
Coverage factor k (2 < k < 3) | 2 | ||||
Extended uncertainty U(y) | 24.552 | ||||
Ripper -Schmitt method | |||||
Type B uncertainty Xm Type B uncertainty Xm/radq Uncertainty u (Xm)B/Xm | U(p) | U(mr) | U(ct) | U(bt) | U(m) |
0.050000 | 0.050000 | 0.030000 | |||
0.014434 | 0.014434 | 0.000000 | 0.000000 | 0.008660 | |
0.002887 | 0.000157 | 0.0000000 | 0.000087 | ||
Resulting relative uncertainty u(y)/y | 0.06067 | ||||
Resulting uncertainty u(y) | 5.593 | ||||
Coverage factor k (2 < k < 3) | 2 | ||||
Extended uncertainty U(y) | 11.185 | ||||
U(p) uncertainties related to accuracy (due to 50 mL pipette use); U(mr) uncertainties related to standard preparation; U(ct) uncertainties related to the calibration curve; U(bt) uncertainties related to balances; U(m) uncertainties related to accuracy (due to burette use). |
Type A standard uncertainties | |||||
X | Spectrophotometric method | Ripper-Schmitt method | |||
1 | 14.00 | 10.88 | |||
2 | 11.52 | 9.60 | |||
3 | 16.44 | 12.16 | |||
4 | 12.80 | 10.24 | |||
5 | 16.60 | 10.24 | |||
6 | 12.44 | 8.96 | |||
7 | 14.20 | 7.68 | |||
8 | 13.12 | 5.12 | |||
9 | 11.55 | 8.32 | |||
10 | 11.68 | 8.32 | |||
Xm | 13.4350 | 9.1520 | |||
Y = 77.4x + 552 | |||||
Standard deviation | 1.8781 | 1.9564 | |||
Relative deviation (sr) | 0.1398 | 0.2138 | |||
Type A uncertainty y = √varianceDegrees of freedom | 0.626 | 0.652 | |||
Type B systematic uncertainties | |||||
Spectrophotometric method | |||||
Type B uncertainty Xm Type B uncertainty Xm/radq Uncertainty u (Xm)B/Xm |
U(p) | U(mr) | U(ct) | U(bt) | U(m) |
0.050000 | 0.000000 | ||||
0.000000 | 0.014434 | 0.000000 | 0.000000 | 0.000000 | |
0.0000000 | 0.000096 | 0.0000000 | 0.0000000 | 0.0000000 | |
Resulting relative uncertainty u(y)/y | 0.13979 | ||||
Resulting uncertainty u(y) | 1.878 | ||||
Coverage factor k (2 < k < 3) | 2 | ||||
Extended uncertainty U(y) | 3.756 | ||||
Ripper -Schmitt method | |||||
Type B uncertainty Xm Type B uncertainty Xm/radq Uncertainty u (Xm)B/Xm | U(p) | U(mr) | U(ct) | U(bt) | U(m) |
0.050000 | 0.050000 | 0.030000 | |||
0.014434 | 0.014434 | 0.000000 | 0.000000 | 0.008660 | |
0.002887 | 0.001577 | 0.0000000 | 0.000087 | ||
Resulting relative uncertainty u(y)/y | 0.21379 | ||||
Resulting uncertainty u(y) | 1.957 | ||||
Coverage factor k (2 < k < 3) | 2 | ||||
U(p) uncertainties related to accuracy (due to 50 mL pipette use); U(mr) uncertainties related to standard preparation; U(ct) uncertainties related to the calibration curve; U(bt) uncertainties related to balances; U(m) uncertainties related to accuracy (due to burette use). |
Standard | X (g/L) | Yfound | Ycalculated | |
High and low concentration red and white and high concentration balsamic wine vinegars (mg/L) | 1 | 19 | 1451.6008 | 1450 |
2 | 38 | 2319.5044 | 2340 | |
3 | 75 | 4022.3055 | 4060 | |
4 | 150 | 7598.7301 | 7560 | |
1 | 19 | 1483.0711 | 1450 | |
2 | 38 | 2323.9193 | 2340 | |
3 | 75 | 4057.2913 | 4060 | |
4 | 150 | 7559.4303 | 7560 | |
1 | 19 | 1474.3658 | 1450 | |
2 | 38 | 2361.0670 | 2340 | |
3 | 75 | 4037.2093 | 4060 | |
4 | 150 | 7549.0236 | 7560 | |
Low concentration balsamic wine vinegar (mg/L) | 1 | 1.88 | 708.8872 | 698 |
2 | 3.75 | 845.173 | 843 | |
3 | 7.5 | 1100.3899 | 1130 | |
4 | 15 | 1726.0638 | 1710 | |
1 | 1.88 | 717.8597 | 698 | |
2 | 3.75 | 846.4987 | 843 | |
3 | 7.5 | 1100.8734 | 1130 | |
4 | 15 | 1726.8831 | 1710 | |
1 | 1.88 | 717.468 | 698 | |
2 | 3.75 | 841.4904 | 843 | |
3 | 7.5 | 1100.2545 | 1130 | |
4 | 15 | 1728.1739 | 1710 |
High and low concentration red and white and high concentration balsamic wine vinegars | |||||
x | hi leverages coefficient |
ei absolute residuals |
eNi normalized residuals |
eSi studentized residuals |
eji standardized residuals |
19.0 | 0.171 | −1.31 | −0.051 | −0.056 | −0.054 |
38.0 | 0.118 | −18.9 | −0.742 | −0.790 | −0.774 |
75.0 | 0.084 | 40.5 | −1.590 | −1.661 | −1.852 |
150 | 0.293 | 40.4 | 1.584 | 1.884 | 2.226 |
19.0 | 0.171 | 30.2 | 1.182 | 1.299 | 1.351 |
38.0 | 0.118 | −14.5 | −0.569 | −0.606 | −0.585 |
75.0 | 0.084 | −5.56 | −0.218 | −0.228 | −0.217 |
150 | 0.293 | 1.12 | 0.044 | 0.052 | 0.049 |
19.0 | 0.171 | 21.5 | 0.841 | 0.924 | 0.917 |
38.0 | 0.118 | 22.6 | 0.888 | 0.945 | 0.90 |
75.0 | 0.084 | −25.6 | −1.005 | −1.050 | −1.057 |
150 | 0.293 | −9.29 | −0.364 | −0.433 | −0.415 |
Low concentration balsamic wine vinegar | |||||
x | hi leverages coefficient |
ei absolute residuals |
eNi normalized residuals |
eSi studentized residuals |
eji standardized residuals |
1.88 | 0.171 | 11.1 | 0.515 | 0.565 | 0.545 |
3.75 | 0.119 | 2.61 | 0.121 | 0.129 | 0.123 |
750 | 0.084 | −32.5 | −1.509 | −1.576 | −1.725 |
15.0 | 0.293 | 12.6 | 0.5186 | 0.697 | 0.678 |
1.88 | 0.171 | 20.1 | 0.932 | 1.023 | 1.026 |
3.75 | 0.119 | 3.93 | 0.183 | 0.195 | 0.185 |
7.50 | 0.84 | −32.0 | −1.486 | −1.553 | −1.691 |
15.0 | 0.293 | 13.4 | 0.625 | 0.743 | 0.725 |
1.88 | 0.171 | 19.7 | 0.914 | 1.003 | 1.004 |
3.75 | 0.119 | −1.08 | −0.050 | −0.053 | −0.050 |
7.50 | 0.084 | −32.6 | −1.515 | −1.583 | −1.735 |
15.0 | 0.293 | 14.7 | 0.685 | 0.814 | 0.799 |
LLOD (mg/L) | LLOQ (mg/L) | Measuring range (mg/L) | |
Low-concentration balsamic wine vinegar | 1.38 | 4.03 | 4.03 ≤ Measuring range ≤ 15 |
Low-concentration red and white and high concentration balsamic wine kinds of vinegar | 2.72 | 6.06 | 6.06 ≤ Measuring range ≤ 150 |
Spectrophotometric method | ||
Sample | ![]() | |
1 | 15.440 | |
2 | 14.570 | |
3 | 15.260 | |
4 | 14.990 | |
5 | 15.270 | |
6 | 15.140 | |
7 | 13.930 | |
8 | 16.750 | |
9 | 14.680 | |
10 | 16.550 | |
Ripper Schmitt method | ||
Sample | ![]() | |
1 | 14.080 | |
2 | 15.360 | |
3 | 16.640 | |
4 | 15.360 | |
5 | 14.080 | |
6 | 16.640 | |
7 | 15.00 | |
8 | 12.00 | |
9 | 15.360 | |
10 | 19.00 |
Spectrophotometric method | ||
Sample | ![]() | |
1 | 57.02 | |
2 | 54.32 | |
3 | 50.42 | |
4 | 51.98 | |
5 | 54.25 | |
6 | 49.46 | |
7 | 53.17 | |
8 | 51.99 | |
9 | 57.05 | |
10 | 53.45 | |
Ripper Schmitt method | ||
Sample | ||
1 | 48.00 | ![]() |
2 | 46.08 | |
3 | 44.16 | |
4 | 42.88 | |
5 | 42.24 | |
6 | 44.80 | |
7 | 41.60 | |
8 | 44.16 | |
9 | 42.24 | |
10 | 47.36 |
Spectrophotometric method | ||
Sample | ![]() | |
1 | 21.02 | |
2 | 20.89 | |
3 | 22.07 | |
4 | 21.45 | |
5 | 22.25 | |
6 | 21.31 | |
7 | 22.14 | |
8 | 22.98 | |
9 | 23.05 | |
10 | 22.87 | |
Ripper Schmitt method | ||
Sample | ![]() | |
1 | 29.18 | |
2 | 28.67 | |
3 | 27.98 | |
4 | 28.03 | |
5 | 28.67 | |
6 | 28.67 | |
7 | 28.67 | |
8 | 29.70 | |
9 | 28.67 | |
10 | 29.55 |
Spectrophotometric method | ||
Sample | ![]() | |
1 | 90.79 | |
2 | 97.33 | |
3 | 98.96 | |
4 | 97.95 | |
5 | 99.15 | |
6 | 99.16 | |
7 | 96.36 | |
8 | 87.39 | |
9 | 90.38 | |
10 | 91.23 | |
Ripper Schmitt method | ||
Sample | ![]() | |
1 | 96.00 | |
2 | 99.20 | |
3 | 95.36 | |
4 | 97.92 | |
5 | 86.40 | |
6 | 86.40 | |
7 | 86.40 | |
8 | 84.48 | |
9 | 98.36 | |
10 | 94.25 |
Spectrophotometric method | ||
Sample | ![]() | |
1 | 14.00 | |
2 | 11.52 | |
3 | 16.44 | |
4 | 12.80 | |
5 | 16.60 | |
6 | 12.44 | |
7 | 14.20 | |
8 | 13.12 | |
9 | 11.55 | |
10 | 11.68 | |
Ripper Schmitt method | ||
Sample | ![]() | |
1 | 10.88 | |
2 | 9.60 | |
3 | 12.16 | |
4 | 10.24 | |
5 | 10.24 | |
6 | 8.96 | |
7 | 7.68 | |
8 | 9.65 | |
9 | 8.32 | |
10 | 8.32 |
Sample | Reproducibility |
White wine vinegar with a low SO2 concentration | 0.8551.86= 0.460 |
White wine vinegar with a high SO2 concentration | 2.502.22= 1.13 |
Red wine vinegar with a low SO2 concentration | 0.8070.560= 1.43 |
Red wine vinegar with a high SO2 concentration | 4.445.59= 0.794 |
Balsamic wine vinegar with a low SO2 concentration | 1.881.35= 1.39 |
Depending on the degree of freedom (n=9) Upper limit of reproducibility = 0.548; Lower limit of reproducibility = 1.480 |
With low SO2 concentration | ||
![]() |
Student's test Hypothesized difference | 0 |
t statistic | 0.09 | |
DF | 9 | |
p-value | 0.9329 | |
With high SO2 concentration | ||
![]() |
Student's test Hypothesized difference | 0 |
t statistic | −9.10 | |
DF | 9 | |
p-value | < 0.0001 | |
y = concentration official meth/colorimetric method x = concentration official meth/colorimetric meth2 |
With low SO2 concentration | ||
![]() |
Student's test Hypothesized difference | 0 |
t statistic | −27.22 | |
DF | 9 | |
p-value | < 0.0001 | |
With high SO2 concentration | ||
![]() |
Student's test Hypothesized difference | 0 |
t statistic | −1.38 | |
DF | 9 | |
p-value | 0.2019 | |
y = concentration official method/colorimetric method x = concentration official method/colorimetric method2 |
With low SO2 concentration | ||
![]() |
Student's test Hypothesized difference | 0 |
t statistic | −8.04 | |
DF | 9 | |
p-value | < 0.0001 | |
y = concentration official method/colorimetric method x = concentration official method/colorimetric method2 |
Vinegar samples | Average deviation | Average of the averages | Relative deviation % |
White wine vinegar with a low SO2 concentration | 0.059 | 15.32 | 0.38 |
White wine vinegar with a high SO2 concentration | −8.959 | 48.83 | 18.34 |
Redwine vinegar with low SO2 concentration | 6.775 | 25.39 | 26.68 |
Red wine vinegar with a high SO2 concentration | −2.693 | 93.52 | 2.88 |
Balsamic wine vinegar with a low SO2 concentration | −4.283 | 11.29 | 37.93 |
Type A standard uncertainties | |||||
X | Spectrophotometric method | Ripper-Schmitt method | |||
1 | 15.44 | 14.08 | |||
2 | 14.57 | 15.36 | |||
3 | 15.26 | 16.64 | |||
4 | 14.99 | 15.36 | |||
5 | 15.27 | 14.08 | |||
6 | 15.14 | 16.64 | |||
7 | 13.93 | 15.00 | |||
8 | 17.10 | 12.00 | |||
9 | 14.68 | 15.36 | |||
10 | 16.55 | 19.00 | |||
Xm | 15.2930 | 15.3520 | |||
Y = 47x + 547 | |||||
Standard deviation | 0.9274 | 1.8625 | |||
Relative deviation (sr) | 0.0606 | 0.1213 | |||
Type A uncertainity = √varianceDegrees of freedom | 0.309 | 0.621 | |||
Type B systematic uncertainties | |||||
Spectrophotometric method | |||||
Type B uncertainty Xm | U(p) | U(mr) | U(ct) | U(bt) | U(m) |
7.500000 | 1.244771 | ||||
Type B uncertainty Xm/radq | 0.000000 | 2.165064 | 0.359334 | 0.000000 | 0.000000 |
Uncertainty u (Xm)B/Xm | 0.0000000 | 0.014434 | 0.054693 | 0.0000000 | 0.0000000 |
Resulting relative uncertainty u(y)/y | 0.08293 | ||||
Resulting uncertainty u(y) | 1.268 | ||||
Coverage factor k (2 < k < 3) | 2 | ||||
Extended uncertainty U(y) | 2.536 | ||||
Ripper -Schmitt method | U(p) | U(mr) | U(ct) | U(bt) | U(m) |
Type B uncertainty Xm | 0.050000 | 0.050000 | 0.030000 | ||
Type B uncertainty Xm/radq | 0.014434 | 0.014434 | 0.000000 | 0.000000 | 0.008660 |
Uncertainty u (Xm)B/Xm | 0.002887 | 0.000940 | 0.0000000 | 0.000087 | |
Resulting relative uncertainty u(y)/y | 0.12136 | ||||
Resulting uncertainty u(y) | 1.863 | ||||
Coverage factor k (2 < k < 3) | 2 | ||||
Extended uncertainty U(y) | 3.726 | ||||
U(p) uncertainties related to accuracy (due to 50 mL pipette use); U(mr) uncertainties related to standard preparation; U(ct) uncertainties related to the calibration curve; U(bt) uncertainties related to balances; U(m) uncertainties related to accuracy (due to burette use). |
Type A standard uncertainties | |||||
X | Spectrophotometric method | Ripper-Schmitt method | |||
1 | 57.02 | 48.00 | |||
2 | 54.32 | 46.08 | |||
3 | 50.42 | 44.16 | |||
4 | 51.98 | 42.88 | |||
5 | 54.25 | 42.24 | |||
6 | 49.46 | 44.80 | |||
7 | 53.17 | 41.60 | |||
8 | 51.99 | 44.16 | |||
9 | 57.05 | 42.24 | |||
10 | 53.45 | 47.36 | |||
Xm | 53.3110 | 44.3520 | |||
Y = 46.6x + 567 | |||||
Standard deviation | |||||
Relative deviation (sr) | |||||
Type B systematic uncertainties | |||||
Spectrophotometric method | |||||
Type B uncertainty Xm Type B uncertainty Xm/radq Uncertainty u (Xm)B/Xm |
U(p) | U(mr) | U(ct) | U(bt) | U(m) |
0.050000 | 0.417939 | ||||
0.000000 | 0.014434 | 0.120649 | 0.000000 | 0.000000 | |
0.0000000 | 0.000096 | 0.120649 | 0.0000000 | 0.0000000 | |
Resulting relative uncertainty u(y)/y | 0.12946 | ||||
Resulting uncertainty u(y) | 6.902 | ||||
Coverage factor k (2 < k < 3) | 2 | ||||
Extended uncertainty U(y) | 13.803 | ||||
Ripper -Schmitt method | |||||
U(p) | U(mr) | U(ct) | U(bt) | U(m) | |
Type B uncertainty Xm | 0.050000 | 0.050000 | 0.030000 | ||
Type B uncertainty Xm/radq | |||||
Uncertainty u (Xm)B/Xm | 0.014434 | 0.014434 | 0.000000 | 0.000000 | 0.008660 |
0.002887 | 0.000325 | 0.0000000 | 0.000087 | ||
Resulting relative uncertainty u(y)/y | 0.05009 | ||||
Resulting uncertainty u(y) | 2.222 | ||||
Coverage factor k (2 < k < 3) | 2 | ||||
Extended uncertainty U(y) | 4.444 | ||||
U(p) uncertainties related to accuracy (due to 50 mL pipette use); U(mr) uncertainties related to standard preparation; U(ct) uncertainties related to the calibration curve; U(bt) uncertainties related to balances; U(m) uncertainties related to accuracy (due to burette use). |
Type A standard uncertainties | |||||
X | Spectrophotometric method | Ripper-Schmitt method | |||
1 | 21.02 | 29.18 | |||
2 | 20.89 | 28.67 | |||
3 | 22.07 | 27.98 | |||
4 | 21.45 | 28.03 | |||
5 | 22.25 | 28.67 | |||
6 | 21.31 | 28.67 | |||
7 | 22.14 | 28.67 | |||
8 | 22.98 | 29.70 | |||
9 | 23.05 | 28.67 | |||
10 | 22.87 | 29.55 | |||
Xm | 22.0040 | 28.7790 | |||
Y = 47x + 547 | |||||
Standard deviation | 0.8072 | 0.5639 | |||
Relative deviation (sr) | 0.367 | 0.0196 | |||
Type A uncertainty y = √varianceDegrees of freedom | 0.269 | 0.188 | |||
Type B systematic uncertainties | |||||
Spectrophotometric method | |||||
Type B uncertainty Xm Type B uncertainty Xm/radq Uncertainty u (Xm)B/Xm | U(p) | U(mr) | U(ct) | U(bt) | U(m) |
0.050000 | 0.417939 | ||||
0.000000 | 0.014434 | 0.120649 | 0.000000 | 0.000000 | |
0.0000000 | 0.000096 | 0.120649 | 0.0000000 | 0.0000000 | |
Resulting relative uncertainty u(y)/y | 0.12610 | ||||
Resulting uncertainty u(y) | 2.775 | ||||
Coverage factor k (2 < k < 3) | 2 | ||||
Extended uncertainty U(y) | 5.550 | ||||
Ripper -Schmitt method | |||||
Type B uncertainty Xm Type B uncertainty Xm/radq Uncertainty u (Xm)B/Xm | U(p) | U(mr) | U(ct) | U(bt) | U(m) |
0.050000 | 0.050000 | 0.030000 | |||
0.014434 | 0.014434 | 0.000000 | 0.000000 | 0.008660 | |
0.002887 | 0.000502 | 0.0000000 | 0.000087 | ||
Resulting relative uncertainty u(y)/y | 0.01981 | ||||
Resulting uncertainty u(y) | 0.570 | ||||
Coverage factor k (2 < k < 3) | 2 | ||||
Extended uncertainty U(y) | 1.140 | ||||
U(p) uncertainties related to accuracy (due to 50 mL pipette use); U(mr) uncertainties related to standard preparation; U(ct) uncertainties related to the calibration curve; U(bt) uncertainties related to balances; U(m) uncertainties related to accuracy (due to burette use). |
Type A standard uncertainties | |||||
X | Spectrophotometric method | Ripper-Schmitt method | |||
1 | 90.79 | 96.00 | |||
2 | 97.33 | 99.20 | |||
3 | 98.96 | 95.36 | |||
4 | 97.95 | 97.92 | |||
5 | 99.15 | 86.40 | |||
6 | 99.16 | 86.40 | |||
7 | 96.36 | 86.40 | |||
8 | 87.39 | 84.48 | |||
9 | 90.38 | 95.36 | |||
10 | 91.23 | 94.25 | |||
Xm | 94.8700 | 92.1770 | |||
Y = 46.6x + 567 | |||||
Standard deviation | 4.4374 | 5.5861 | |||
Relative deviation (sr) | 0.0468 | 0..0606 | |||
Type A uncertainty y = √varianceDegrees of freedom | 1.479 | 1.862 | |||
Type B systematic uncertainties | |||||
Spectrophotometric method | |||||
Type B uncertainty Xm Type B uncertainty Xm/radq Uncertainty u (Xm)B/Xm | U(p) | U(mr) | U(ct) | U(bt) | U(m) |
0.050000 | 0.417939 | ||||
0.000000 | 0.014434 | 0.120649 | 0.000000 | 0.000000 | |
0.0000000 | 0.00096 | 0.120649 | 0.0000000 | 0.0000000 | |
Resulting relative uncertainty u(y)/y | 0.12940 | ||||
Resulting uncertainty u(y) | 12.276 | ||||
Coverage factor k (2 < k < 3) | 2 | ||||
Extended uncertainty U(y) | 24.552 | ||||
Ripper -Schmitt method | |||||
Type B uncertainty Xm Type B uncertainty Xm/radq Uncertainty u (Xm)B/Xm | U(p) | U(mr) | U(ct) | U(bt) | U(m) |
0.050000 | 0.050000 | 0.030000 | |||
0.014434 | 0.014434 | 0.000000 | 0.000000 | 0.008660 | |
0.002887 | 0.000157 | 0.0000000 | 0.000087 | ||
Resulting relative uncertainty u(y)/y | 0.06067 | ||||
Resulting uncertainty u(y) | 5.593 | ||||
Coverage factor k (2 < k < 3) | 2 | ||||
Extended uncertainty U(y) | 11.185 | ||||
U(p) uncertainties related to accuracy (due to 50 mL pipette use); U(mr) uncertainties related to standard preparation; U(ct) uncertainties related to the calibration curve; U(bt) uncertainties related to balances; U(m) uncertainties related to accuracy (due to burette use). |
Type A standard uncertainties | |||||
X | Spectrophotometric method | Ripper-Schmitt method | |||
1 | 14.00 | 10.88 | |||
2 | 11.52 | 9.60 | |||
3 | 16.44 | 12.16 | |||
4 | 12.80 | 10.24 | |||
5 | 16.60 | 10.24 | |||
6 | 12.44 | 8.96 | |||
7 | 14.20 | 7.68 | |||
8 | 13.12 | 5.12 | |||
9 | 11.55 | 8.32 | |||
10 | 11.68 | 8.32 | |||
Xm | 13.4350 | 9.1520 | |||
Y = 77.4x + 552 | |||||
Standard deviation | 1.8781 | 1.9564 | |||
Relative deviation (sr) | 0.1398 | 0.2138 | |||
Type A uncertainty y = √varianceDegrees of freedom | 0.626 | 0.652 | |||
Type B systematic uncertainties | |||||
Spectrophotometric method | |||||
Type B uncertainty Xm Type B uncertainty Xm/radq Uncertainty u (Xm)B/Xm |
U(p) | U(mr) | U(ct) | U(bt) | U(m) |
0.050000 | 0.000000 | ||||
0.000000 | 0.014434 | 0.000000 | 0.000000 | 0.000000 | |
0.0000000 | 0.000096 | 0.0000000 | 0.0000000 | 0.0000000 | |
Resulting relative uncertainty u(y)/y | 0.13979 | ||||
Resulting uncertainty u(y) | 1.878 | ||||
Coverage factor k (2 < k < 3) | 2 | ||||
Extended uncertainty U(y) | 3.756 | ||||
Ripper -Schmitt method | |||||
Type B uncertainty Xm Type B uncertainty Xm/radq Uncertainty u (Xm)B/Xm | U(p) | U(mr) | U(ct) | U(bt) | U(m) |
0.050000 | 0.050000 | 0.030000 | |||
0.014434 | 0.014434 | 0.000000 | 0.000000 | 0.008660 | |
0.002887 | 0.001577 | 0.0000000 | 0.000087 | ||
Resulting relative uncertainty u(y)/y | 0.21379 | ||||
Resulting uncertainty u(y) | 1.957 | ||||
Coverage factor k (2 < k < 3) | 2 | ||||
U(p) uncertainties related to accuracy (due to 50 mL pipette use); U(mr) uncertainties related to standard preparation; U(ct) uncertainties related to the calibration curve; U(bt) uncertainties related to balances; U(m) uncertainties related to accuracy (due to burette use). |
Standard | X (g/L) | Yfound | Ycalculated | |
High and low concentration red and white and high concentration balsamic wine vinegars (mg/L) | 1 | 19 | 1451.6008 | 1450 |
2 | 38 | 2319.5044 | 2340 | |
3 | 75 | 4022.3055 | 4060 | |
4 | 150 | 7598.7301 | 7560 | |
1 | 19 | 1483.0711 | 1450 | |
2 | 38 | 2323.9193 | 2340 | |
3 | 75 | 4057.2913 | 4060 | |
4 | 150 | 7559.4303 | 7560 | |
1 | 19 | 1474.3658 | 1450 | |
2 | 38 | 2361.0670 | 2340 | |
3 | 75 | 4037.2093 | 4060 | |
4 | 150 | 7549.0236 | 7560 | |
Low concentration balsamic wine vinegar (mg/L) | 1 | 1.88 | 708.8872 | 698 |
2 | 3.75 | 845.173 | 843 | |
3 | 7.5 | 1100.3899 | 1130 | |
4 | 15 | 1726.0638 | 1710 | |
1 | 1.88 | 717.8597 | 698 | |
2 | 3.75 | 846.4987 | 843 | |
3 | 7.5 | 1100.8734 | 1130 | |
4 | 15 | 1726.8831 | 1710 | |
1 | 1.88 | 717.468 | 698 | |
2 | 3.75 | 841.4904 | 843 | |
3 | 7.5 | 1100.2545 | 1130 | |
4 | 15 | 1728.1739 | 1710 |
High and low concentration red and white and high concentration balsamic wine vinegars | |||||
x | hi leverages coefficient |
ei absolute residuals |
eNi normalized residuals |
eSi studentized residuals |
eji standardized residuals |
19.0 | 0.171 | −1.31 | −0.051 | −0.056 | −0.054 |
38.0 | 0.118 | −18.9 | −0.742 | −0.790 | −0.774 |
75.0 | 0.084 | 40.5 | −1.590 | −1.661 | −1.852 |
150 | 0.293 | 40.4 | 1.584 | 1.884 | 2.226 |
19.0 | 0.171 | 30.2 | 1.182 | 1.299 | 1.351 |
38.0 | 0.118 | −14.5 | −0.569 | −0.606 | −0.585 |
75.0 | 0.084 | −5.56 | −0.218 | −0.228 | −0.217 |
150 | 0.293 | 1.12 | 0.044 | 0.052 | 0.049 |
19.0 | 0.171 | 21.5 | 0.841 | 0.924 | 0.917 |
38.0 | 0.118 | 22.6 | 0.888 | 0.945 | 0.90 |
75.0 | 0.084 | −25.6 | −1.005 | −1.050 | −1.057 |
150 | 0.293 | −9.29 | −0.364 | −0.433 | −0.415 |
Low concentration balsamic wine vinegar | |||||
x | hi leverages coefficient |
ei absolute residuals |
eNi normalized residuals |
eSi studentized residuals |
eji standardized residuals |
1.88 | 0.171 | 11.1 | 0.515 | 0.565 | 0.545 |
3.75 | 0.119 | 2.61 | 0.121 | 0.129 | 0.123 |
750 | 0.084 | −32.5 | −1.509 | −1.576 | −1.725 |
15.0 | 0.293 | 12.6 | 0.5186 | 0.697 | 0.678 |
1.88 | 0.171 | 20.1 | 0.932 | 1.023 | 1.026 |
3.75 | 0.119 | 3.93 | 0.183 | 0.195 | 0.185 |
7.50 | 0.84 | −32.0 | −1.486 | −1.553 | −1.691 |
15.0 | 0.293 | 13.4 | 0.625 | 0.743 | 0.725 |
1.88 | 0.171 | 19.7 | 0.914 | 1.003 | 1.004 |
3.75 | 0.119 | −1.08 | −0.050 | −0.053 | −0.050 |
7.50 | 0.084 | −32.6 | −1.515 | −1.583 | −1.735 |
15.0 | 0.293 | 14.7 | 0.685 | 0.814 | 0.799 |