i-fast validatietraject MOBISPEC

Transcription

1 4 th User Group Meeting 12/10/2017 i-fast validatietraject MOBISPEC MeBioS Biophotonics Group Nghia Nguyen, Karlien D huys, Bart De Ketelaere, Wouter Saeys nghia.nguyendotrong@kuleuven.be kalien.dhuys@kuleuven.be bart.deketelaere@kuleuven.be wouter.saeys@kuleuven.be

2 Marelec case: Minced meat quality control (extra analysis)

3 Research aims Extra analysis Test possibility of using the range nm for minced meat quality control (fat content): Beef and Pork Samples: 7 fat contents x 9 samples = 63 samples for beef or pork, measured at 3 temperatures: 4 C, 6 C, and 8 C 0% 10% 20% 30% 0% 10% 20% 30% 40% 50% 100% Minced beef meat with increasing fat content (%w/w) 40% 50% 100% Minced pork meat with increasing fat content (%w/w)

4 Spectral variations: Beef and Pork Beef Removed 0% 10% 20% 30% Hb/HbO 2 Fat Water 40% 50% 100% Minced beef meat with increasing fat content (%w/w) Removed Hb/HbO 2 Pork 0% 10% 20% 30% Fat 40% 50% 100% Water Minced pork meat with increasing fat content (%w/w)

5 Partial Least Squares Regression Models 0% fat Matlab + PLS toolbox Model calibration PLS Model 100% fat X: Spectra matrix Y: Actual fat content (%w/w) Model validation Apply PLS Model X: New spectra Model prediction accuracy: Y: predicted fat content (%w/w) Implemented in MATLAB with PLS toolbox

6 PLS model calibration: cross-validation 1 st step 2 nd step Last step 6 Validation block Block = 9 spectra of all samples having a fat content: Leave one group out cross validation Calibration block Block = all remaining spectra used for constructing PLS model Model prediction accuracy: Implemented in MATLAB with PLS toolbox

7 PLS model performance (Beef: nm) CV: 6 C VAL: 4 C VAL: 8 C

8 PLS model performance (Beef: nm) CV: 6 C VAL: 4 C VAL: 8 C

9 PLS model performance (Pork: nm) CV: 6 C VAL: 4 C VAL: 8 C

10 PLS model performance (Pork: nm) CV: 6 C VAL: 4 C VAL: 8 C

11 Conclusions Extra analysis When using only the range nm for fat prediction: Comparable prediction performance for beef mixtures Improved prediction performance for pork mixtures Important range for prediction: nm: Fat peak: around 930 nm Water peak: around 970 nm

12 Marelec case: Fish fillet freshness

13 Research aims Test possibility of using Vis-NIR spectroscopy for fish fillet freshness evaluation: Cod and Salmon Experiment design: 30 fillet samples for Cod or Salmon Freshly cut from whole fishes in Delhailze Measured at 4 storage times (at 1 C, closely tighted bag): 0, 3, 6, and 9 days Salmon fillets Cod fillets

14 Fish fillet samples: Cod Side 1 Day 0 Side 1 Day 3 Side 1 Day 6 Side 1 Day 9 Side 2 Day 0 Side 2 Day 3 Side 2 Day 6 Side 2 Day 9 Acceptable Unacceptable Unchanged appearance (under human eyes) Bad texture Commercial products: freshness at 3 days Bad smell

15 Fish fillet samples: Salmon Side 1 Day 0 Side 1 Day 3 Side 1 Day 6 Side 1 Day 9 Side 2 Day 0 Side 2 Day 3 Side 2 Day 6 Side 2 Day 9 Acceptable Unacceptable Unchanged appearance (under human eyes) Bad texture Commercial products: freshness at 4 days Bad smell

16 Spectroscopy setup Fish fillet Measuring head: One light source illuminating from bottom Circularly arranged detectors Computer-controlled for data acquisition Spectrometers: Zeiss Corona Fibre (Zeiss, Germany): nm and nm

17 Measured locations Each Salmon Fillet Each Cod Fillet Side 1 Side Side 2 Side 2 Each location, each wavelength range: 30 fillets x 4 storage times = 120 spectra over storage Measured with skin: evaluate possibility to measure whole salmon fish in the future Measured area: having diameter of 6 cm

18 Spectral variation: Cod Location 1 1 After 6, 9 days: Unchanged appearance (under human eyes) Bad texture Bad smell Mean spectra (n = 30) Mb/MbO 2 O-H Mean spectra ( nm): Observed changes over time O-H protein

19 Spectral variation: Cod Location 1 1 After 6, 9 days: Unchanged appearance (under human eyes) Bad texture Bad smell O-H Mean spectra (n = 30) Mean spectra ( nm): Observed changes over time from 0 to 6 days C-H C-H O-H N-H C-H ArC-H

20 Spectral variation: Salmon Location 1 1 After 6, 9 days: Unchanged appearance (under human eyes) Bad texture Bad smell Mean spectra (n = 30) O-H Mean spectra ( nm): Small changes over time O-H Pigments

21 Spectral variation: Salmon Location 1 1 After 6, 9 days: Unchanged appearance (under human eyes) Bad texture Bad smell O-H Mean spectra (n = 30) Mean spectra ( nm): Small changes over time from 0 to 6 days C-H C-H O-H N-H C-H ArC-H Bigger changes at 9 days

22 Spectral variation: Salmon Location 4 4 After 6, 9 days: Unchanged appearance (under human eyes) Bad texture Bad smell Mean spectra (n = 30) Mean spectra ( nm): Changes over time in the range nm Mb/MbO 2 O-H protein

23 Partial Least Squares (PLS) Regression Models

24 Partial Least Squares Regression Models Matlab + PLS toolbox Model calibration PLS Model X: Spectra matrix Y: Actual storage time Model validation X: New spectra Model prediction accuracy: Implemented in MATLAB with PLS toolbox Apply PLS Model Y: predicted storage time

25 PLS model performance: cross-validation 1 st step 2 nd step Last step 25 Validation block Block = 4 spectra of 4 storage times on the same fillet: Leave one fillet over time out cross validation Calibration block Block = all remaining spectra used for constructing PLS model Model prediction accuracy: Implemented in MATLAB with PLS toolbox

26 Cod Location 1: nm (left) vs nm (right) selected 1 1 Fat protein pigment C-H

27 Cod nm: Location 1 (left) vs Location 2 (right) Better prediction 1 2 Fat Fat pigment protein pigment protein

28 Salmon - Location 1: nm (left) vs nm (right) selected 1 1 protein pigment C-H

29 Salmon nm: Location 1 (left) vs Location 2 (right) Better prediction 1 2 protein pigment protein pigment

30 Salmon nm: Location 3 (left) vs Location 4 (right) Better prediction 3 4 protein protein

31 Conclusions Cod fillet: Comparable storage day prediction performance between SWIR ( nm) and VNIR ( nm) VNIR is preferable: cheaper sensor costs Location 1 (skin side) gave better prediction than Location 2 (tissue side) Salmon fillet: Slightly better prediction performance for VNIR ( nm) than SWIR ( nm) VNIR is preferable: cheaper sensor costs Flesh measurement: Location 2 (fat belly side) gave better prediction than Location 1 (tissue side) Measurement through skin possible: Location 4 (fat belly side) gave better prediction than Location 3 (tissue side)

32 Goedegebuur case: beef carcass ph

33 Research aims Test possibility of using Vis-NIR spectroscopy for on-site beef carcass ph measurement Spectroscopic measurements: 30 beef carcasses (3 locations per carcass) at Goedegebuur production plant On-site measurements using mobile spectroscopic setup Off-line data analysis (model calibration, prediction)

34 Diffuse reflectance sensing head Diffuse reflected light Diffuse illumination Halogen light source Beef carcass Diffuse reflection spectral acquisition Spectrometer: Zeiss Corona Fibre Vis/NIR ( nm and nm)

35 Spectra of beef carcasses ( nm) O-H Fat Protein Water MbO 2

36 Spectra of beef carcasses ( nm) O-H C-H C-H O-H N-H C-H Ar-CH

37 Partial Least Squares Regression Models Carcass 1 Matlab + PLS toolbox Model calibration PLS Model Carcass n X: Spectra matrix Y: Actual ph values Model validation X: New spectra Model prediction accuracy: Implemented in MATLAB with PLS toolbox Apply PLS Model Y: predicted ph values

38 PLS model performance: cross-validation 1 st step 2 nd step Last step 47 Validation block Block = 3 spectra of 3 locations on a carcass: Leave one carcass out cross validation Calibration block Block = all remaining spectra used for constructing PLS model Model prediction accuracy: Implemented in MATLAB with PLS toolbox

39 PLS model performance: nm Good prediction performance with Leave-One-Carcass-Out cross-validation

40 PLS model performance: nm C-H Ar-CH O-H C-H C-H O-H N-H Absorption of functional groups used for model prediction

41 PLS model performance: nm Worse prediction performance with Leave-One-Carcass-Out cross-validation than using the range nm

42 PLS model performance: nm Mostly red color used for model prediction: robustness?

43 Conclusions ph beef carcass prediction: Possible to predict ph values using SWIR ( nm) or VNIR ( nm) SWIR: PLS model used relevant chemical absorption group information more robust VNIR: PLS model mostly used color information not robust?

44 Dossche Mills Case: Export flour quality (continued)

45 Research aims Test possibility of using NIR spectroscopy for detecting quality deteriorated flours during export Flour samples: 42 packages of 2-3kg each Good 4w25 C 8w25 C 12w25 C 4w35 C 8w35 C 12w35 C 1w50 C Real bad sample ,20 14,21 15,22 16,23 17,24 18,25 19, w refers to week, number indicates sample number

46 NIR Lab spectrometer MPA Spectrometer (Bruker Optics, Germany) Accurate measurement Area scanning, cup rotated 64 scans/measurement, averaging to obtain one spectrum 3 cups/package in total: 126 spectra acquired for 42 samples or packages

47 Handheld spectrometers MicroPHAZIR (Thermo Fisher Scientific, USA) NR2.2 (Spectral Engines, Finland) NR1.7 (Spectral Engines, Finland) Price: ~ ( nm) Price: ~ 5500 ( nm) Price: ~ 5500 ( nm) DLPNIRNANOEVM (Texas Instruments, USA) 3 measurements/package in total: 126 spectra acquired for 42 samples or packages Price: ~ 900 ( nm)

48 Flour quality classification- PLSDA model N Good flours Bad flours Model Calibration PLSDA Model 0 1 X: spectral data Y Real-life bad flours Modal Validation PLSDA Model X: spectral data Y Implemented in MATLAB with PLS toolbox

49 PLSDA model calibration: Cross-Validation 1 st step 2 nd step Last step 58 Validation block Block = 3 spectra of one package Calibration block Block = all remaining spectra Cross-Validation provides predicted classes for 3 spectra in each red block

50 PLSDA Prediction Results - Bruker Cross-Validation Perfect prediction for all flour samples Good flours: 12 packages x 3 spectra = 36 spectra Bad flours: 24 packages x 3 spectra = 72 spectra Validation Perfect prediction for all real-life bad flours Real-life bad flours: 6 packages x 3 spectra = 18 spectra

51 PLSDA Prediction Results - MicroPHAZIR 7 wrong predictions good flours (p01-1, p03-1, p05-1, p11-2, p13-2) Cross-Validation Good flours: 12 packages x 3 spectra = 36 spectra 6 wrong predictions: 4w25 C (p13-2), 8w35 C (p17-1), 1w50 C (p19-1), 8w25 C (p21-1), 12w25 C (p22-1) Validation Bad flours: 24 packages x 3 spectra = 72 spectra (p13-2): package 13, 2 measurements out of 3 ones were wrongly predicted Perfect prediction for all real-life bad flours Real-life bad flours: 6 packages x 3 spectra = 18 spectra

52 PLSDA Prediction Results - TI 2 wrong predictions good flours (p01-1, p05-1) Cross-Validation Good flours: 12 packages x 3 spectra = 36 spectra 13 wrong predictions: 4w25 C (p13-3), 8w25 C (p14-3), 12w25 C (p15-3), 4w35 C (p16-3), 8w35 C (p17-1), Bad flours: 24 packages x 3 spectra = 72 spectra Validation Perfect prediction for all real-life bad flours Real-life bad flours: 6 packages x 3 spectra = 18 spectra

53 PLSDA Prediction Results NR22 3 wrong predictions good flours (p01-3) Cross-Validation Good flours: 12 packages x 3 spectra = 36 spectra 13 wrong predictions: 4w25 C (p13-3), 8w25 C (p14-3), 12w25 C (p15-3), 4w35 C (p16-3), 8w35 C (p17-1), Bad flours: 24 packages x 3 spectra = 72 spectra Validation Perfect prediction for all real-life bad flours Real-life bad flours: 6 packages x 3 spectra = 18 spectra

54 PLSDA Prediction Results NR17 7 wrong predictions good flours (p10-3,p11-3,p12-1) Cross-Validation Good flours: 12 packages x 3 spectra = 36 spectra 2 wrong predictions: 12w25 C (p15-1), 8w25 C (p21-1) Bad flours: 24 packages x 3 spectra = 72 spectra Validation Perfect prediction for all real-life bad flours Real-life bad flours: 6 packages x 3 spectra = 18 spectra

55 Future plan Measurements using Foss spectrometer at Dosche Mills: Ascorbic acid quantification (ppm levels) for flours Export flours quality control

56 Bioorg Case: Bacillus sp. detection

57 1. Experimental target Search fluorescence signals of bacillus vegetable cells Compare fluorescence signals between bacillus vegetable cells and L-tryptophan (>99%) Compare fluorescence signals between bacillus vegetable cells and spores

58 Fluorescence spectrometer Microplate Reader SpectraMax M2e (Molecular Devices, USA) Excitation: nm Emission: nm Resolution: 1 nm Operation in either absorption or fluorescence measurements

59 2. Method Note: This method couldn t control: sample concentration before measure turbidity Black plate

60 2. Method L-Tryptophan: purity > 99% are bought from Sigma Diluted L-tryptophan 1mM in distilled water Take 300µl L-tryptophan 1mM to measure with fluorescence spectroscopy Bacillus spores: from Bioorg company Diluted 0.1 % spores in distilled water Take 300µl to measure with fluorescence spectroscopy PCA is grinded in small pieces and suspended in distilled water.

61 2. Method Excitation and Emission Matrix (EEM): Excitation wavelength (nm) Emission range of wavelength measured (nm) Excitation wavelength (nm) Emission range of wavelength measured (nm) Excitation wavelength (nm) Emission range of wavelength measured (nm) Reference: Sivananthan Sarasanandarajah, 2007 : Multiwavelength fluoresence studies of Bacillus bacterial spores

62 2. Method Reference information: Tryptophan: Excitation at 280 nm Emission at 300 nm nm Bacillus spores: Excitation at 290 & 300 nm Emission at 330 nm 560 nm Reference: Sivananthan Sarasanandarajah, 2007 : Multiwavelength fluoresence studies of Bacillus bacterial spores

63 3. Results Bacillus sp. Vegetative cells after incubation (48h, 30ºC) Strong vegetative cells Haven t formed spores Bacillus colony on PCA Bacillus sp. Under microscope Under fluorescence microscope at 470nm (blue light): the screen of microscope is black no see fluoresence signals at 470nm

64 1. Result First experiment: search fluorescence of bacillus vegetative cells Strong fluorescence signals Emission range: nm Excitation: 270, 280,290 nm The greatest intensity is red and the least intensity is blue

65 3. Results Repeated experiment: search fluorescence of bacillus vegetative cells Lighter fluorescence signals because of different concentration but range of emission nm and excitation 280, 290nm The greatest intensity is red and the least intensity is blue

66 3. Results Spore experiment: This method may not give fluorescence signals of spore 0.1% because of: - low concentration - If increase high concentration caused turbidity of solution which interfere result. The greatest intensity is red and the least intensity is blue

67 3. Results L-Tryptophan 1mM fluorescence signals Strong fluorescence signals Emission range nm Excitation: 270, 280 nm The greatest intensity is red and the least intensity is blue

68 3. Results Solid PCA medium at excitation from nm : Strong fluorescence signals in range of emission nm and excitation 270, 280nm The greatest intensity is red and the least intensity is blue

69 3. Results PCA in distilled water at excitation from nm : Strong fluorescence signals in emission nm and excitation 270, 280nm If PCA suspended in samples will interfere fluorescence signals The greatest intensity is red and the least intensity is blue

70 4. Discussion Detect fluorescence signals of bacillus vegetative cells Spore solution 0.1% did not give fluorescence signals Solid PCA fluorescence signals will interfere the results Supported reference: Grandi C, Dalzoppo D, Vita C, Fontana A, 1980 May- Fluorescence properties of neutral protease from Bacillus subtilis For bacillus subtilis: excitation 295nm emission maximum 320nm caused by tryptophan residues in neutral protease

71 4.Discussion Supported reference: Daisuke Tsuru, James Dwight Mcconn, Kerry T.Yasunobu,1964 Journal of biological chemistry Vol 240. No 6 June 1965 For component of neutral protease: including tryptophan,tyrosin, phenylalanine and other amino acids. Sivananthan Sarasanandarajah,2007 B.Sc (Hons), P.G.Dip.Sc, M.Sc at the University of Canterbury Christchurch, New Zealand nm excites the aromatic tyrosine, tryptophan and phenylalanine and emission between nm.

72 5. Future plan Change the way to extract bacillus colonies without using PCA Monitor fluorescence signals of spore solution over time Test with UV spectrophotometer + UV light

73 Online course Fundamentals and Applications of Near Infrared Technology

74 Online course Fundamentals and Applications of Near Infrared Technology Organized by the ICNIRS (International Conference on Near Infrared Spectroscopy) Education Committee Theoretical lectures, practical exercises, interactive selflearning exercises and tools, virtual visits to laboratories, etc. Covers basic NIR concepts, such as applications of NIR in a variety of situations in private and public enterprises

75 Online course Fundamentals and Applications of Near Infrared Technology Module I. Introduction and Basic Principles o Introduction, instrumentation, sample preparation, sampling statistics, mathematical pre-treatment of spectra, Module II. Quantitative and Qualitative NIRS multivariate modelling o Calibration development, maintenance of calibrations, qualitative and quantitative modeling, Module III. NIRS applications o High dry matter products, liquid and semi-liquid products, high moisture products, precision farming, food security, Module IV. Open Innovation and beyond NIRS o Spectral imaging technologies for food safety, NIR in dairy industry, NIR in-line analysis of solids with probes and sensor heads,

76 Online course Fundamentals and Applications of Near Infrared Technology Full programme (30 lectures of about 1h): course-1/programme Timing: 1/11 15/12 (courses are recorded) Cost: bank commissions Possible to enrol from 15/10

77 Vragen?