Prediction of beef chemical composition by NIR Hyperspectral ... - ATB

707172737475767778798081828384858687888990919293949596979899100101102(Meadow Meats, Rathdowney, Co. Laois, Ireland). At 24 h post-mortem, three muscles (M.longissimus dorsi (LD), M. semitendinosus (ST) and M. psoas major (PM)) were dissectedfrom each carcass and then sliced to 1-inch thick slices by a mechanical slicer. The sliceswere labelled and vacuum packed and stored at 4°C until the next day when qualityparameters were measured. Moisture and fat contents were analysed using the Smart Trac(CEM Corporation, North Carolina, USA), and protein content was measured using a LECOFP-428 Nitrogen Determinator (LECO Instruments Ltd., UK). After chemical measurements,the samples were minced and imaged again in the hyperspectral system.The configuration of the developed hyperspectral imaging system is explained in details inElMasry et al. (2011). In practise, beef sample was placed on the conveying stage to bescanned line by line using 10 ms exposure time to build a hyperspectral image (R 0 ) which isthen corrected against dark and white references.The average spectrum of each sample was extracted by segmentation to locate the leanparts of the beef sample as the main region of interest (ROI). Only one average spectrumwas used to represent each sample and the same routine was repeated for all hyperspectralimages of beef samples. Predictive partial least squares (PLS) regression models weredeveloped for each constituent so that these attributes can be predicted in the future directlyfrom the measured spectra. The accuracy and the predictive capabilities of the model wereevaluated based on coefficient of determination in calibration (determination in cross-validation (and the root mean square error by cross-validation (RMSECV).3. Results and Discussion3.1. Spectral features2RC), coefficient of2RCV), the root mean square error of calibration (RMSEC)The hyperspectral image described as I(x,y,λ) can be viewed either as a separate spatialsub-image I(x,y) at each wavelength (λ) as shown in Figure 1a, or as a spectrum I(λ) at anypixel (x,y) as shown in Figure 1b. The peak at 974 nm was apparent in the beef sample dueto the water absorption bands related to O–H stretching second overtones, whereas asecond peak at 1211 nm was due to the fat absorption related to C–H stretching secondovertone.The performance of the developed PLS regression models for predicting the major chemicalcomposition of beef samples in calibration and leave-one-out cross-validation wasreasonably good.

103104105106107108109110111112113114115116117118119120121122123124125126127FIGURE 1 (a) Assortment of some NIR sub-images at wavelengths indicated, (b) Spectral3.2. Prediction of chemical constituentsfeatures of the examined muscles.Table 1 shows the main statistical parameters resulted from each model in cross validationand prediction samples. The results indicated that the PLSR models were very efficient inpredicting these chemical constituents with a determination coefficient (2Rcv) of 0.91, 0.89and 0.91 using 9, 8, 10 factors for fat, moisture and protein respectively. When the modelused in predicting these constituents in the samples of a prediction set, the model gave goodprediction ability with determination coefficient (2Rp) of 0.89, 0.86 and 0.75 for water, fat andprotein, respectively. However, the predictability of protein content might be considereduncertain as the value of2RP(0.75) is rather small and the number of latent factors washigher compared with the other two constituents. The lack of a strong predictability forprotein in meat suing spectral analyses is not new, and several authors have reported similarresults (Alomar 2003).It was very advantageous to select significant variables during a multivariate regression inorder to improve the predictive ability of the model and to augment the processing speed.These feature-related wavelengths identified from the PLSR’s weighted regressioncoefficients of each constituent indicated that eight wavelengths (934, 1048, 1108, 1155,1185, 1212, 1265, 1379 nm) were defined as the most relevant wavelengths in predictingwater contents. Similarly, seven wavelengths (934, 978, 1078, 1138, 1215, 1289, 1413 nm)were selected for fat and ten wavelengths (924, 937, 1018, 1048, 1108, 1141, 1182, 1221,1615, and 1665 nm) were selected for efficient prediction of protein. The results shown inTable 1 revealed that the predictability of these models is still good indicating the robustness

128129130131132133134135of the developed models. Water, fat and protein were predicted with determinationcoefficient (2Rp) of 0.89, 0.84 and 0.86 with standard error of prediction (SEP) of 0.46, 0.65and 0.29%, respectively. More importantly, the protein model was much better comparedwith that developed with the full spectral range.TABLE 1 Performance of the developed PLSR model in predicting water, fat and proteincontents in beef samples using the full spectral range and the selected feature-relatedwavelengths.No ofCross-validationPredictionConstituentwavelengthsLFsR 2 cv SECV(%) R 2 p SEP(%)WaterFatProtein(1) 237 9 0.91 0.48 0.89 0.47(2) 8 6 0.89 0.51 0.89 0.46(1) 237 8 0.89 0.65 0.86 0.62(2) 7 6 0.88 0.66 0.84 0.65(1) 237 10 0.91 0.27 0.75 0.39(2) 10 9 0.88 0.31 0.86 0.29136137138139140141142143144145146147148149150151152(1) Models developed using the full spectral range (237 wavelengths)(2) Models developed using the selected feature-related wavelengths (8, 7 and 10wavelengths for water, fat and protein content, respectively).AcknowledgementsFunding by the Irish Department of Agriculture, Fisheries and Food through FoodInstitutional Research Measure (FIRM) strategic research initiative is acknowledged.4. ReferencesElMasry, G., Sun, D.-W., Allen, P. (2011). Non-destructive determination of water-holdingcapacity in fresh beef by using NIR hyperspectral imaging. Food Research International,44 (9): 2624-2633.Prieto, N., Roehe, R., Lavín, P., Batten, G. & Andrés, S. (2009). Application of near infraredreflectance spectroscopy to predict meat and meat products quality: A review. MeatScience, 83 (2), 175-186.Wold, J. P., Johansen, Ib-R., Haugholt, K, H., Tschudi, J., Thielemann, J., Segtnan, V,H.,Narum, B. & Wold, E. (2006). Non-destructive tranreflectance near infrared

153154155156157158159160161162163164165166167168169170171172173174175imaging for representative on-line sampling of dried salted coalfish (bacalao). Journalof Near Infrared Spectroscopy, 14, 59-66.Burger, J. & Geladi, P. (2006). Hyperspectral NIR imaging for calibration and prediction, acomparison between image and spectrometer data for studying organic and biologicalsamples. The Analyst, 131, 1152-1160.ElMasry, G., and Wold, J. P. (2008). High-Speed Assessment of Fat and Water ContentDistribution in Fish Fillets Using Online Imaging Spectroscopy. Journal of Agriculturaland Food Chemistry, 56(17), 7672-7677.Ottestad, S., Høy, M., Stevik, A., and Wold, J.P. (2009). Prediction of ice fraction and fatcontent in super-chilled salmon by non-contact interactance near infrared imaging.Journal of Near-Infrared Spectroscopy, 17(2), 77-87.Kamruzzaman, M., ElMasry, G., Sun, D-W, Allen, P. (2012). Prediction of some qualityattributes of lamb meat using NIR hyperspectral imaging and multivariate analysis.Analytica Chimica Acta, 714 (10): 57-67.Barbin D., ElMasry G., Sun, D.-W. & Allen P. (2012). Predicting quality and sensoryattributes using near-infrared hyperspectral imaging. Analytica Chimica Acta, 714 (10):57-67.Alomar, D., Gallo, C., Castañeda, M. & Fuchslocher, R. (2003). Chemical and discriminantanalysis of bovine meat by near infrared reflectance spectroscopy (NIRS). MeatScience, 63, 441-450.ElMasry, G. & Sun, D-W. (2010). Meat Quality Assessment Using a Hyperspectral ImagingSystem. In: Hyperspectral Imaging for Food Quality Analysis and Control (Edited byDa-Wen Sun), PP. 273-294, Academic Press / Elsevier, San Diego, California, USA.