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March 2 nd 2016

Monitoring chemical and sensory parameters of tomato product with near infrared spectroscopy Dong Sun , Jordi Cruz Sánchez , Joan Casals Missió , Joan Simó, Josep Sabaté Reboll, Manel Alcalà Bernàrdez. March 2 nd 2016. Who am I?.

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March 2 nd 2016

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  1. Monitoring chemical and sensory parameters of tomato product with near infrared spectroscopy Dong Sun, Jordi Cruz Sánchez, Joan CasalsMissió, Joan Simó, Josep Sabaté Reboll, ManelAlcalàBernàrdez March 2nd 2016

  2. Whoam I? 1. INTRODUCTION 2. OBJECTIVES 3. METHODOLOGY 4. RESULTS 5.CONCLUSIONS Jordi Cruz University lecturer from Escola UniversitàriaSalesiana de Sarrià and external researcher in Applied Chemometrics Group at UniversitatAutònoma de Barcelona

  3. Where I comefrom? 1. INTRODUCTION 2. OBJECTIVES 3. METHODOLOGY 4. RESULTS 5.CONCLUSIONS CATALUNYA a country that sees how the sun rises from the Mediterranean Sea

  4. Where I comefrom? 1. INTRODUCTION 2. OBJECTIVES 3. METHODOLOGY 4. RESULTS 5.CONCLUSIONS I come from CATALUNYA

  5. Where I comefrom? 1. INTRODUCTION 2. OBJECTIVES 3. METHODOLOGY 4. RESULTS 5.CONCLUSIONS CATALUNYA is country of ancestral traditions

  6. Where I comefrom? 1. INTRODUCTION 2. OBJECTIVES 3. METHODOLOGY 4. RESULTS 5.CONCLUSIONS CATALUNYA is a country of gastronomy.

  7. Pa amb tomàquet 1. INTRODUCTION 2. OBJECTIVES 3. METHODOLOGY 4. RESULTS 5.CONCLUSIONS Best tomato quality must be chosen CATALUNYA’s most known recipe.

  8. Quality control 1. INTRODUCTION 2. OBJECTIVES 3. METHODOLOGY 4. RESULTS 5.CONCLUSIONS Control Parameters Chemical Sensorial Fructose Glucose Brix Dry matter Sweetness Acidity Flavor Explosiveness Aroma Crispiness Floury Skin perception

  9. Quality control 1. INTRODUCTION 2. OBJECTIVES 3. METHODOLOGY 4. RESULTS 5.CONCLUSIONS Chemical methods Tasting panel TRADITIONAL METHODOLOGIES • Time, Money & chemical reagents consuming • Sample preparation • For sensory methods a tasting panel is needed

  10. No sample preparation • Many modes of measuring • Low cost analysis • Fast • Robust • Nondestructive 1. INTRODUCTION 2. OBJECTIVES 3. METHODOLOGY 4. RESULTS 5.CONCLUSIONS WHY NIR? Chemical & Physical & other Information Multi parametric analisys

  11. 1. INTRODUCTION 2. OBJECTIVES 3. METHODOLOGY 4. RESULTS 5.CONCLUSIONS OBJECTIVES • Determination of physical, chemical and sensorial parameters in ripe tomatoes • Development of fast analytical methodologies • Demonstrate that NIR spectroscopy combined with spectral pretreatment, Partial Component Analysis, and Partial Least Squares Regression can be an alternative technique to classical and sensorial analysis

  12. 1. INTRODUCTION 2. OBJECTIVES 3. METHODOLOGY 4. RESULTS 5.CONCLUSIONS TOMATO SAMPLES LIQUID EXTRACT • PURÉE There were 316 juice samples and 270 puree samples in total. Flesh, seeds, and peels were extracted from purée Crushed tomatoes

  13. 1. INTRODUCTION 2. OBJECTIVES 3. METHODOLOGY 4. RESULTS 5.CONCLUSIONS NEAR INFRARED APPLICATION NIR SPECTROSCOPY TRANSFLECTANCE REFLECTANCE LIQUID EXTRACT • PURÉE

  14. 1. INTRODUCTION 2. OBJECTIVES 3. METHODOLOGY 4. RESULTS 5.CONCLUSIONS Workflow of model development Data structure • Data set split • Spectra pretreatment • Spectral range selection • Outlier selection • Calibration • Validation 1100-2500 nm Sensorial Parameters Chemical Parameters SAMPLES NIR SPECTRA 4 7 699

  15. 1. INTRODUCTION 2. OBJECTIVES 3. METHODOLOGY 4. RESULTS 5.CONCLUSIONS Spectra pretreatment Absorbance Absorbance Wavelength (nm.) Wavelength (nm.) a) Tomato liquid extract b) Tomato purée

  16. 1. INTRODUCTION 2. OBJECTIVES 3. METHODOLOGY 4. RESULTS 5.CONCLUSIONS Pretreated Tomato liquid extract spectra Absorbance Absorbance Absorbance Wavelength (nm.) Wavelength (nm.) Wavelength (nm.) SG smth+ 1stDer MSC +1st Der SG smth+ 2nd Der • Pretreated Tomato purée spectra Absorbance Absorbance Absorbance Wavelength (nm.) Wavelength (nm.) Wavelength (nm.) SG smth+ 1stDer MSC +1st Der SG smth+ 2nd Der

  17. 1. INTRODUCTION 2. OBJECTIVES 3. METHODOLOGY 4. RESULTS 5.CONCLUSIONS Tomato liquid extract models

  18. 1. INTRODUCTION 2. OBJECTIVES 3. METHODOLOGY 4. RESULTS 5.CONCLUSIONS Fructose (g/l) 7 PLS components Range :1100-1834nm,2120-2320nm Pretreatment: SG 2D 11points 2order Calibration range: 4,52-15,14(g/L) RMSEC=0,41 g/l Prediction range: 4,77- 14,98(g/L) RMSEP=0,52 g/l Calibration Prediction

  19. 1. INTRODUCTION 2. OBJECTIVES 3. METHODOLOGY 4. RESULTS 5.CONCLUSIONS Glucose (g/l) 7 PLS components Range :1100-1834nm, 2120-2320nm Pretreatment: SG 2D 11points 2order Calibration range: 2,43-15,47(g/L) RMSEC=0,48 g/l Prediction range: 4,40- 14,26(g/L) RMSEP=0,51 g/l Calibration Prediction

  20. 1. INTRODUCTION 2. OBJECTIVES 3. METHODOLOGY 4. RESULTS 5.CONCLUSIONS Tomato purée models

  21. 1. INTRODUCTION 2. OBJECTIVES 3. METHODOLOGY 4. RESULTS 5.CONCLUSIONS Fructose (g fructose/l ) 3 PLS components Band Range :1100-1850nm,2094-2238nm Pretreatment: SG 2D 11points 2order Calibration range: 0,67-3,82(g/100g) RMSEC=0,13g/100g Prediction range:1,38-3,52 g/100g RMSEP=0,08 g/100g Calibration Prediction

  22. 1. INTRODUCTION 2. OBJECTIVES 3. METHODOLOGY 4. RESULTS 5.CONCLUSIONS Glucose(g glucose/l) 3 PLS components BandRange :1100-1850nm,2094-2238nm Pretreatment: SG 2D 11points 2order Calibration range: 0,64-3,94(g/l) RMSEC=0,13 g/100g Prediction range:1,19-3,55(g/l) RMSEP=0,09 g/100g Calibration Prediction

  23. 1. INTRODUCTION 2. OBJECTIVES 3. METHODOLOGY 4. RESULTS 5.CONCLUSIONS Brix (brix degrees) 3 PLS components Band Range :1100-1834nm,2120-2320nm Pretreatment: SG 2D 11 points 2 order Calibration range: 3,6-10,6 (brix degrees) RMSEC=0,31brix degrees Prediction range:4,2-9,8 (brix degrees) RMSEP=0,25 brix degrees Calibration Prediction

  24. 1. INTRODUCTION 2. OBJECTIVES 3. METHODOLOGY 4. RESULTS 5.CONCLUSIONS Dry Matter (g dry matter/100g fresh matter) 5 PLS components Band Range : 2200-2340nm Pretreatment: SG 2D 11points 2order Calibration range: 5,16-11,55 (g/100g) RMSEC=0,36 g/100g Prediction range:5,50-11,00(g/100g) RMSEP= 0,26 g/100g Calibration Prediction

  25. 1. INTRODUCTION 2. OBJECTIVES 3. METHODOLOGY 4. RESULTS 5.CONCLUSIONS Summary table for models of chemical parameters

  26. 1. INTRODUCTION 2. OBJECTIVES 3. METHODOLOGY 4. RESULTS 5.CONCLUSIONS Tomato sensory models

  27. 1. INTRODUCTION 2. OBJECTIVES 3. METHODOLOGY 4. RESULTS 5.CONCLUSIONS Sweetness scale 0-10 3 PLS components Band Rang:2120-2320nm Pretreatment: 2D 7point Calibration range: 2,03-10,07 RMSEC=0,95 Prediction range:3,59-9,45 RMSEP=0,83 Calibration Prediction

  28. 1. INTRODUCTION 2. OBJECTIVES 3. METHODOLOGY 4. RESULTS 5.CONCLUSIONS Flavourscale 0-10 7 PLS components Band Rang: 2120-2320nm Pretreatment: 2D 7point Calibration range: 2,3-7,7 RMSEC=0,66 Prediction range:3,55-6,68 RMSEP=0,73 Calibration Prediction

  29. 1. INTRODUCTION 2. OBJECTIVES 3. METHODOLOGY 4. RESULTS 5.CONCLUSIONS Aroma scale 0-10 3 PLS components Band Rang: 1100-2498nm Pretreatment: none Calibration range: 1,62-7,76 RMSEC=0,95 Prediction range:1,7-6,51 RMSEP=0,77 Calibration Prediction

  30. 1. INTRODUCTION 2. OBJECTIVES 3. METHODOLOGY 4. RESULTS 5.CONCLUSIONS Floury scale 0-10 3 PLS components Band Rang: 2120-2320nm Pretreatment: 2D 7point Calibration range: 1,11-5,96 RMSEC=0,66 Prediction range:1,22-4,65 RMSEP=0,62 Calibration Prediction

  31. 1. INTRODUCTION 2. OBJECTIVES 3. METHODOLOGY 4. RESULTS 5.CONCLUSIONS Crispiness scale 0-10 5 PLS components Band Rang: 2120-2320nm Pretreatment: 2D 7point Calibration range: 1,65-7,96 RMSEC=0,96 Prediction range:1,89-6,33 RMSEP=0,84 Calibration Prediction

  32. 1. INTRODUCTION 2. OBJECTIVES 3. METHODOLOGY 4. RESULTS 5.CONCLUSIONS Skin perception scale 0-10 3 PLS components Band Rang: 1100-2498nm Pretreatment: 2D 11point Calibration range: 4,71-8,89 RMSEC=0,73 Prediction range: 5,69-8,49 RMSEP=0,79 Calibration Prediction

  33. 1. INTRODUCTION 2. OBJECTIVES 3. METHODOLOGY 4. RESULTS 5.CONCLUSIONS Acidity scale 0-10 4 PLS components Band Rang: 2120-2320nm Pretreatment: 2D 7point Calibration range: 1,79-8,1 RMSEC=0,96 Prediction range: 2,71-7,68 RMSEP=1,04 Calibration Prediction

  34. 1. INTRODUCTION 2. OBJECTIVES 3. METHODOLOGY 4. RESULTS 5.CONCLUSIONS Explosiveness scale 0-10 5 PLS components Band Rang: :2120-2320nm Pretreatment: 2D 7point Calibration range: 1,5-2,56 RMSEC=0,05 Prediction range: 1,75-2,44 RMSEP=0,20 Calibration Prediction

  35. 1. INTRODUCTION 2. OBJECTIVES 3. METHODOLOGY 4. RESULTS 5.CONCLUSIONS Summary table for models of sensorial parameters

  36. 1. INTRODUCTION 2. OBJECTIVES 3. METHODOLOGY 4. RESULTS 5.CONCLUSIONS • NIRS has shown to be a powerful tool for quality control in tomato manufacturing. • The main goal of this study is the capacity of prediction with PLS models from up to 12 chemical and sensory parameters with an unique spectrum. • The results of validation for chemical and sensorial parameters show us low residual and error values and good predictive performances. • In the models for sensorial parameters, the ability of NIRS & chemometrics to perform the experience of human experts has been demonstrated.

  37. Acknowlegements

  38. Monitoring chemical and sensory parameters of tomato product with near infrared spectroscopy Dong Sun, Jordi Cruz Sánchez, Joan CasalsMissió, Joan Simó, Josep Sabaté Reboll, ManelAlcalàBernàrdez Thanks!!! Any question??? March 3rd 2016

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