Non-Destructive Quality Assessment of Tomato Paste by Using Portable Mid-Infrared Spectroscopy and Multivariate Analysis
Abstract
:1. Introduction
2. Materials and Methods
2.1. Tomato Paste Samples
2.2. Reference Analyses
2.3. Mid-Infrared Analysis
2.4. Partial Least Squares Regression (PLSR) Analysis
3. Results and Discussion
3.1. Reference Values in Tomato Paste Samples
3.2. Spectral Information of Tomato Paste Samples.
3.3. PLSR Calibration Models
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Company | Year | Number of Samples | NTSS | pH | Bostwick | TA | Serum Viscosity c | Lycopene | Glucose | Fructose | Ascorbic Acid | Citric Acid | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
A | 2015 | 120 | range | 25.6–36.0 | 4.3–4.5 | 2.5–10.8 | 1.0–1.9 | 2.3–2.6 | |||||
Avg a ± std b | 30.7 ± 2.8 | 4.4 ± 0.0 | 5.1 ± 2.0 | 1.5 ± 0.2 | 2.4 ± 0.1 | ||||||||
2016 | 150 | range | 25.8–37.1 | 4.2–4.4 | 1.6–9.1 | 1.1–2.0 | 2.1–2.9 | 70.3–116.6 | 75.9–117.6 | 56.8–97.0 | 6.3–10.6 | ||
avg ± std | 30.1 ± 2.8 | 4.3 ± 0.1 | 4.1 ± 1.9 | 1.5 ± 0.2 | 2.6 ± 0.2 | 93.3 ± 10.3 | 96.4 ± 9.6 | 77.6 ± 8.9 | 8.4 ± 0.9 | ||||
2017 | 196 | range | 25.4–37.0 | 4.2–4.5 | 0.8–11.9 | 1.0–1.8 | 2.2–2.8 | 74.1–128.2 | 75.2–130.5 | 49.0–110.7 | 6.4–11.7 | ||
avg ± std | 28.6 ± 2.4 | 4.4 ± 0.0 | 3.5 ± 2.2 | 1.3 ± 0.2 | 2.5 ± 0.1 | 92.0 ± 11.5 | 92.3 ± 11.0 | 79.4 ± 10.9 | 8.4 ± 0.9 | ||||
2019 | 87 | range | 25.7–38.0 | 4.2–4.5 | 1.5–9.2 | 1.1–2.1 | 2.3–2.8 | 400.6–869.1 | |||||
avg ± std | 31.1 ± 2.8 | 4.4 ± 0.1 | 5.4 ± 2.1 | 1.4 ± 0.2 | 2.5 ± 0.2 | 644.5 ± 112.1 | |||||||
General | 553 | range | 25.4–38.0 | 4.2–4.5 | 0.8–11.9 | 1.0–2.1 | 2.1–2.9 | 70.3–128.2 | 75.2–130.5 | 49.0–110.7 | 6.3–11.7 | ||
avg ± std | 29.8 ± 2.9 | 4.4 ± 0.1 | 4.3 ± 2.2 | 1.4 ± 0.2 | 2.5 ± 0.1 | 92.6 ± 11.0 | 94.1 ± 10.6 | 78.7 ± 10.1 | 8.4 ± 0.9 | ||||
B | 2016 | 79 | range | 28.5–37.5 | 4.1–4.5 | 4.0–7.6 | 1.3 -2.2 | 1.9–2.4 | 88.1–120.1 | 92.1–121.4 | 59.7–104.8 | 6.8–10.4 | |
avg ± std | 32.2 ± 2.9 | 4.3 ± 0.1 | 5.6–1.2 | 1.6 ± 0.3 | 2.1 ± 0.2 | 104.0 ± 9.0 | 104.9 ± 7.8 | 75.6 ± 8.9 | 8.4 ± 0.9 | ||||
2017 | 116 | range | 28.0–36.2 | 4.1–4.5 | 1.1–6.4 | 1.3 -1.9 | 1.8–2.4 | 87.0–128.0 | 94.0–128.8 | 55.8–98.4 | 5.9–10.0 | ||
avg ± std | 30.6 ± 1.2 | 4.4 ± 0.1 | 4.8 ± 1.1 | 1.6 ± 0.1 | 2.1 ± 0.2 | 102.4 ± 6.0 | 101 ± 5.2 | 78.8 ± 11.1 | 8.3 ± 0.7 | ||||
2019 | 103 | range | 24.1–38.1 | 4.2–4.5 | 1.2–8.5 | 1.0–1.9 | 1.8–2.6 | ||||||
avg ± std | 31.6 ± 4.3 | 4.4 ± 0.1 | 4.3 ± 2.1 | 1.5 ± 0.2 | 2.2 ± 0.2 | ||||||||
General | 298 | range | 24.1–38.1 | 4.1–4.5 | 1.1–8.5 | 1.0–2.2 | 1.8–2.6 | 87.0–128.0 | 92.1–128.8 | 55.8–104.8 | 5.9–10.4 | ||
avg ± std | 31.5 ± 3.0 | 4.3 ± 0.1 | 4.8 ± 1.6 | 1.6 ± 0.2 | 2.1 ± 0.2 | 102.0 ± 7.4 | 102.6 ± 6.6 | 77.5 ± 10.4 | 8.3 ± 0.8 | ||||
C | 2016 | 222 | range | 27.8–37.5 | 4.1–4.5 | 2.3–7.9 | 1.5–2.2 | 1.9–2.5 | 81.2–122.6 | 89.2–128.0 | 32.6–100.7 | 7.8–9.6 | |
avg ± std | 31.0 ± 1.9 | 4.3 ± 0.1 | 4.4 ± 1.3 | 1.8 ± 0.2 | 2.2 ± 0.2 | 95.9 ± 8.8 | 101.0 ± 7.9 | 65.5 ± 12.8 | 8.6 ± 0.4 | ||||
2017 | 290 | range | 26.0–36.5 | 4.1–4.5 | 0.8–7.1 | 1.3–2.4 | 1.9–2.7 | 71.0–122.9 | 77.8–123.8 | 13.5–109.8 | 7.1–11.2 | ||
avg ± std | 29.1 ± 2.8 | 4.4 ± 0.1 | 3.1 ± 1.6 | 1.7 ± 0.2 | 2.4 ± 0.2 | 90.6 ± 12.6 | 94.4 ± 12.1 | 55.0 ± 19.9 | 8.5 ± 1.1 | ||||
2019 | 110 | range | 26.1–31.6 | 4.2–4.5 | 1.0–4.9 | 1.3–2.3 | 1.9–2.7 | 614.3–829.3 | |||||
avg ± std | 29.2 ± 2.1 | 4.4 ± 0.1 | 3.0 ± 1.2 | 1.7 ± 0.2 | 2.3 ± 0.2 | 690.9 ± 43.6 | |||||||
General | 622 | range | 26.0–37.5 | 4.1–4.5 | 0.8–7.9 | 1.3–2.4 | 1.9–2.7 | 71.0–127.9 | 77.8–128.0 | 13.5–109.8 | 7.1–11.2 | ||
avg ± std | 29.8 ± 2.6 | 4.4 ± 0.1 | 3.5 ± 1.6 | 1.8 ± 0.2 | 2.3 ± 0.2 | 92.9 ± 11.4 | 97.3 ± 10.9 | 59.6 ± 17.9 | 8.6 ± 0.8 | ||||
D | 2015 | 47 | range | 25.0–26.6 | 4.2–4.4 | 1.1–2.4 | 1.3–1.5 | 2.6–2.7 | |||||
avg ± std | 25.9 ± 0.3 | 4.3 ± 0.0 | 1.8 ± 0.4 | 1.4 ± 0.0 | 2.7 ± 0.0 | ||||||||
2016 | 48 | range | 25.3–26.5 | 4.4–4.5 | 2.3–2.9 | 1.3–1.4 | 2.9–3.0 | 76.9–84.6 | 78.1–89.0 | 43.9–63.4 | 5.9–7.1 | ||
avg ± std | 26 ± 0.3 | 4.4 ±0.0 | 2.6 ± 0.2 | 1.4 ± 0.0 | 3.0 ± 0.0 | 80.7 ± 1.8 | 83.5 ± 2.7 | 50.8 ± 5.3 | 6.5 ± 0.3 | ||||
2017 | 203 | range | 25.1–28.5 | 4.3–4.5 | 1.0–2.9 | 1.2–1.6 | 2.4–2.9 | 67.5–99.3 | 74.7–100.6 | 12.1–72.2 | 6.6–8.8 | ||
avg ± std | 26.2 ± 0.8 | 4.4 ± 0.0 | 1.9 ± 0.4 | 1.4 ± 0.1 | 2.7 ± 0.1 | 80.7 ± 6.9 | 82.4 ± 3.9 | 38.0 ± 13.0 | 7.4 ± 0.4 | ||||
2019 | 72 | range | 25.3–28.3 | 4.3–4.5 | 1.4–2.2 | 1.3–1.5 | 2.5–2.9 | ||||||
avg ± std | 26.5 ± 1.0 | 4.4 ± 0.0 | 1.9 ± 0.2 | 1.4 ± 0.1 | 2.7 ± 0.1 | ||||||||
General | 370 | range | 25.0–28.5 | 4.2–4.5 | 1.0–2.9 | 1.2–1.6 | 2.4–3.0 | 67.5–104.3 | 74.7–100.6 | 12.1–72.2 | 5.9–8.8 | ||
avg ± std | 26.2 ± 0.8 | 4.4 ± 0.0 | 2.0 ± 0.5 | 1.4 ± 0.1 | 2.7 ± 0.1 | 80.7 ± 6.2 | 82.6 ± 3.7 | 40.4 ± 12.9 | 7.2 ± 0.5 |
Parameter | Calibration Model | External Validation Model | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
Range | N a | Factor | SECV b | Rcv c | Range | N d | SEP e | RPre f | RPD g | |
NTSS (°Brix) | 24.1–38.1 | 1436 | 3 | 0.44 | 0.99 | 25.7–37.5 | 359 | 0.40 | 0.99 | 7.3 |
pH | 4.14–4.49 | 1419 | 6 | 0.04 | 0.85 | 4.19–4.49 | 355 | 0.04 | 0.83 | 1.8 |
Bostwick Consistency (cm) | 0.8–7.9 | 1382 | 5 | 0.55 | 0.94 | 1.0–7.7 | 345 | 0.58 | 0.96 | 2.9 |
Titratable Acidity (% Citric) | 0.99–2.40 | 1406 | 6 | 0.08 | 0.94 | 1.12–2.27 | 352 | 0.09 | 0.93 | 2.8 |
Serum Viscosity (log cSt) | 1.81–2.99 | 1304 | 6 | 0.08 | 0.96 | 1.85–2.99 | 326 | 0.08 | 0.96 | 3.5 |
Lycopene (mg/kg) | 400.6–869.1 | 138 | 6 | 35.75 | 0.93 | 483.4–851.1 | 35 | 35.11 | 0.93 | 2.7 |
Glucose (g/L) | 67.5–128.2 | 1043 | 5 | 3.16 | 0.96 | 68.9–122.6 | 261 | 3.39 | 0.97 | 3.5 |
Fructose (g/L) | 74.7–128.8 | 1032 | 4 | 3.11 | 0.96 | 75.4–128.0 | 258 | 3.88 | 0.96 | 2.9 |
Ascorbic Acid (mg/100 g) | 12.1–110.7 | 1040 | 6 | 6.99 | 0.94 | 16.7–105.6 | 260 | 7.32 | 0.93 | 2.7 |
Citric Acid (g/100 g) | 5.9–11.2 | 1031 | 5 | 0.27 | 0.96 | 6.3–10.5 | 258 | 0.27 | 0.96 | 3.4 |
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Aykas, D.P.; Rodrigues Borba, K.; Rodriguez-Saona, L.E. Non-Destructive Quality Assessment of Tomato Paste by Using Portable Mid-Infrared Spectroscopy and Multivariate Analysis. Foods 2020, 9, 1300. https://doi.org/10.3390/foods9091300
Aykas DP, Rodrigues Borba K, Rodriguez-Saona LE. Non-Destructive Quality Assessment of Tomato Paste by Using Portable Mid-Infrared Spectroscopy and Multivariate Analysis. Foods. 2020; 9(9):1300. https://doi.org/10.3390/foods9091300
Chicago/Turabian StyleAykas, Didem Peren, Karla Rodrigues Borba, and Luis E. Rodriguez-Saona. 2020. "Non-Destructive Quality Assessment of Tomato Paste by Using Portable Mid-Infrared Spectroscopy and Multivariate Analysis" Foods 9, no. 9: 1300. https://doi.org/10.3390/foods9091300