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Non-destructive assessment of instrumental and sensory tenderness of lamb meat using NIR hyperspectral imaging

FOOD CHEMISTRY 2013年第1期141卷 389-396页

作者： Kamruzzaman, Mohammed ElMasry, Gamal Sun, Da-Wen Allen, Paul Natl Univ Ireland Univ Coll Dublin Agr & Food Sci Ctr Sch Biosyst Engn Dublin 4 Ireland TEAGASC AFRC Dublin 15 Ireland

The purpose of this study was to develop and test a hyperspectral imaging system (900-1700 nm) to predict instrumental and sensory tenderness of lamb meat. Warner-Bratzler shear force (WBSF) values and sensory scores by trained panellists were collected as the indicator of instrumental and sensory tenderness, respectively. Partial least squares regression models were developed for predicting instrumental and sensory tenderness with reasonable accuracy (R-cv = 0.84 for WBSF and 0.69 for sensory tenderness). Overall, the results confirmed that the spectral data could become an interesting screening tool to quickly categorise lamb steaks in good (i.e. tender) and bad (i.e. tough) based on WBSF values and sensory scores with overall accuracy of about 94.51% and 91%, respectively. successive projections algorithm (SPA) was used to select the most important wavelengths for WBSF prediction. Additionally, textural features from Gray Level Co-occurrence Matrix (GLCM) were extracted to determine the correlation between textural features and WBSF values. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Sensory analysis Warner-Bratzler Shear force Near-infrared hyperspectral imaging successive projections algorithm

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Potential of hyperspectral imaging for non-invasive determination of mechanical properties of prawn (Metapenaeus ensis)

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JOURNAL OF FOOD ENGINEERING 2014年 136卷 64-72页

作者： Dai, Qiong Cheng, Jun-Hu Sun, Da-Wen Zeng, Xin-An S China Univ Technol Coll Light Ind & Food Sci Guangzhou 510641 Guangdong Peoples R China Natl Univ Ireland Univ Coll Dublin Agr & Food Sci Ctr Dublin 4 Ireland

The potential of using hyperspectral imaging in the wavelength region of visible and short-wave near infrared range (400-1000 nm) was investigated for the rapid and non-invasive determination of mechanical properties in different parts of prawns (Metapenaeus ensis). Hyperspectral cubes were acquired at the second and fourth segments of prawn and their mean spectral data were extracted. The quantitative relationship between the spectral data and their corresponding reference mechanical properties (measured by Instron universal testing machine) were established by partial least square regression (PLSR) and least-squares support vector machines (LS-SVM) algorithms, respectively. successive projections algorithm (SPA) was carried out to select the most important and effective wavelengths, which was an optimization process for improving the performance of established models. Then the optimized models were built using PLSR and LS-SVM based on the selected wavelengths and their performances were compared to find the best model for predicting mechanical properties of prawn. As a result, the SPA-LS-SVM model was considered as the best predictive model to determine the hardness, gumminess and chewiness values of prawn with correlation coefficient of prediction (R-p) of 0.8489, 0.8096, and 0.8596 and root mean square error of prediction (RMSEP) of 0.1465, 0.1445, and 0.1258, respectively. Visualization of distribution maps for the mechanical properties within the region of interests (ROI) of prawns was also presented. The overall results revealed that hyperspectral imaging technique had a great ability for predicting the mechanical properties of prawn rapidly and non-invasively. (c) 2014 Elsevier Ltd. All rights reserved.

关键词： Prawn Mechanical properties Hyperspectral imaging successive projections algorithm Least-squares support vector machines

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Application of Vis-NIR hyperspectral imaging in classification between fresh and frozen-thawed pork Longissimus Dorsi muscles

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INTERNATIONAL JOURNAL OF REFRIGERATION 2015年 50卷 10-18页

作者： Ma, Ji Pu, Hongbin Sun, Da-Wen Gao, Wenhong Qu, Jia-Huan Ma, Kai-Yue S China Univ Technol Coll Light Ind & Food Sci Guangzhou 510641 Guangdong Peoples R China Univ Coll Dublin Agr & Food Sci Ctr Dublin 4 Ireland

Fresh and frozen-thawed (F-T) pork meats were classified by Vis-NIR hyperspectral imaging. Eight optimal wavelengths (624, 673, 460, 588, 583, 448, 552 and 609 nm) were selected by successive projections algorithm (SPA). The first three principal components (PCs) obtained by principal component analysis (PCA) accounted for over 99.98% of variance. Gray-level-gradient co-occurrence matrix (GLGCM) was applied to extract 45 textural features from the PC images. The correct classification rate (CCR) was employed to evaluate the performance of the partial least squares-discriminate analysis (RLS-DA) models, by using (A) the reflected spectra at full wavelengths and (B) those at the optimal wavelengths, (C) the extracted textures based on the PC images, and (D) the fused variables combining spectra at the optimal wavelengths and textures. The results showed that the best CCR of 97.73% was achieved by applying (D), confirming the high potential of textures for fresh and F-T meat discrimination. (C) 2014 Elsevier Ltd and IIR. All rights reserved.

关键词： Hyperspectral imaging Pork Fresh Frozen-thawed successive projections algorithm Gray-level-gradient co-occurrence matrix Partial least squares-discriminate analysis Correct classification rate

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A NOVEL METHOD FOR DETECTION OF PIERIS RAPAE LARVAE ON CABBAGE LEAVES USING NIR HYPERSPECTRAL IMAGING

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APPLIED ENGINEERING IN AGRICULTURE 2016年第4期32卷 311-316页

作者： Wu, X. Zhang, W. Qiu, Z. Cen, H. He, Y. Zhejiang Univ Coll Biosyst Engn & Food Sci Hangzhou Zhejiang Peoples R China

Pest detection is important in agricultural production. In this study, near-infrared (NIR) hyperspectral imaging technology was applied to detect 4th instar Pieris rapae larvae on cabbage leaves. After hyperspectral imaging acquisition (1000-1600 nm), successive projections algorithm (SPA) was implemented to select effective wavelengths (EWs). Partial least squares discriminant analysis (PLS-DA) and back-propagation neural network (BPNN) models were developed based on 13 selected EWs to distinguish between leaves and larvae, both yielding acceptable results of correlation coefficient in the calibration set (RC) above 0.98 and classification accuracy in the prediction set above 96%. In terms of computation time, the developed SPA-PLS-DA model was chosen for pixel-wise detection of larvae in mixed samples and achieved accurate visual results. The promising results indicated that it is feasible to use NIR hyperspectral imaging to detect Pieris rapae larvae accurately and intuitively. This technology will be helpful in the early pest control.

关键词： Hyperspectral imaging Pieris rapae larvae detection successive projections algorithm

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Identification of pesticide varieties by detecting characteristics of Chlorella pyrenoidosa using Visible/Near infrared hyperspectral imaging and Raman microspectroscopy technology

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WATER RESEARCH 2016年第Nov.1期104卷 432-440页

作者： Shao, Yongni Li, Yuan Jiang, Linjun Pan, Jian He, Yong Dou, Xiaoming Zhejiang Univ Coll Biosyst Engn & Food Sci Hangzhou 310058 Zhejiang Peoples R China East China Univ Sci & Technol Inst Photon & Biomed Grad Sch Sci Shanghai Peoples R China

The main goal of this research is to examine the feasibility of applying Visible/Near-infrared hyper spectral imaging (Vis/NIR-HSI) and Raman microspectroscopy technology for non-destructive identification of pesticide varieties (glyphosate and butachlor). Both mentioned technologies were explored to investigate how internal elements or characteristics of Chlorella pyrenoidosa change when pesticides are applied, and in the meantime, to identify varieties of the pesticides during this procedure. successive projections algorithm (SPA) was introduced to our study to identify seven most effective wavelengths. With those wavelengths suggested by SPA, a model of the linear discriminant analysis (LDA) was established to classify the pesticide varieties, and the correct classification rate of the SPA-LDA model reached as high as 100%. For the Raman technique, a few partial least squares discriminant analysis models were established with different preprocessing methods from which we also identified one processing approach that achieved the most optimal result. The sensitive wavelengths (SWs) which are related to algae's pigment were chosen, and a model of LDA was established with the correct identification reached a high level of 90.0%. The results showed that both Vis/NIR-HSI and Raman micro spectroscopy techniques are capable to identify pesticide varieties in an indirect but effective way, and SPA is an effective wavelength extracting method. The SWs corresponding to microalgae pigments, which were influenced by pesticides, could also help to characterize different pesticide varieties and benefit the variety identification. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Visible/Near-infrared hyperspectral imaging Raman microspectroscopy Pesticide varieties successive projections algorithm Linear discriminant analysis

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Non-destructive prediction of thiobarbituric acid reactive substances (TSARS) value for freshness evaluation of chicken meat using hyperspectral imaging

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FOOD CHEMISTRY 2015年 179卷 175-181页

作者： Xiong, Zhenjie Sun, Da-Wen Pu, Hongbin Xie, Anguo Han, Zhong Luo, Man S China Univ Technol Coll Light Ind & Food Sci Guangzhou 510641 Guangdong Peoples R China Natl Univ Ireland Univ Coll Dublin Agr & Food Sci Ctr Food Refrigerat & Computerised Food Technol Dublin 4 Ireland

This study examined the potential of hyperspectral imaging (HSI) for rapid prediction of 2-thiobarbituric acid reactive substances (TBARS) content in chicken meat during refrigerated storage. Using the spectral data and the reference values of TBARS, a partial least square regression (PLSR) model was established and yielded acceptable results with regression coefficients in prediction (R-p) of 0.944 and root mean squared errors estimated by prediction (RMSEP) of 0.081. To simplify the calibration model, ten optimal wavelengths were selected by successive projections algorithm (SPA). Then, a new SPA-PLSR model based on the selected wavelengths was built and showed good results with R-p of 0.801 and RMSEP of 0.157. Finally, an image algorithm was developed to achieve image visualization of TBARS values in some representative samples. The encouraging results of this study demonstrated that HSI is suitable for determination of TBARS values for freshness evaluation in chicken meat. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Hyperspectral imaging Non-destructive analysis Chicken meat TEARS Freshness successive projections algorithm Partial least square regression

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Digital image-based tracing of geographic origin, winemaker, and grape type for red wine authentication

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FOOD CHEMISTRY 2020年第0期312卷 126060-000页

作者： de Lima, Carlos Monteiro Sousa Fernandes, David Douglas Pereira, Giuliano Elias Gomes, Adriano de Araujo Ugulino de Araujo, Mario Cesar Goncalves Dias Diniz, Paulo Henrique Univ Fed Paraiba Dept Quim POB 5093 BR-58051970 Joao Pessoa Paraiba Brazil Empresa Brasileira Pesquisa Agr Ctr Pesquisa Agr Trop Semiarido EMBRAPA BR-56302970 Petrolina PE Brazil Univ Fed Rio Grande do Sul Inst Quim BR-90650001 Porto Alegre RS Brazil Univ Fed Oeste Bahia Programa Posgrad Quim Pura & Aplicada BR-47810059 Barreiras BA Brazil

This work proposes the development of a simple, fast, and inexpensive methodology based on color histograms (obtained from digital images), and supervised pattern recognition techniques to classify red wines produced in the Sao Francisco Valley (SFV) region to trace geographic origin, winemaker, and grape variety. PCA-LDA coupled with HSI histograms correctly differentiated all of the SFV samples from the other geographic regions in the test set;SPA-LDA selecting just 10 variables in the Grayscale + HSI histogram achieved 100% accuracy in the test set when classifying three different SFV winemakers. Regarding the three grape varieties, SPA-LDA selected 15 variables in the RGB histogram to obtain the best result, misclassifying only 2 samples in the test set. Pairwise grape variety classification was also performed with only 1 misclassification. Besides following the principles of Green Chemistry, the proposed methodology is a suitable analytical tool;for tracing origins, grape type, and even (SFV) winemakers.

关键词： Red wine Cabernet Sauvignon Syrah Touriga Nacional Geographical origin indication Color histogram Digital images successive projections algorithm

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Nondestructive determination of IMP content in chilled chicken based on hyperspectral data combined with chemometrics

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International Journal of Agricultural and Biological Engineering 2022年第1期15卷 277-284页

作者： Yangyang Wang Hongju He Shengqi Jiang Hanjun Ma School of Food Science Henan Institute of Science and TechnologyXinxiang 453003HenanChina

This study was conducted to investigate the potential of hyperspectral imaging technique(900-1700 nm)for nondestructive determination of inosinic acid(IMP)in *** images of chicken flesh samples were acquired,and their mean spectra within the images were *** quantitative relationship between the mean spectra and reference IMP value was fitted by partial least squares(PLS)regression algorithm.A PLS model(MAS-PLS)built with moving average smoothing(MAS)spectra showed better performance in predicting IMP content,leading to correlation coefficients(RP)of 0.951,root mean square error(RMSEP)of 0.046 mg/g,and residual predictive deviation(RPD)of *** coefficient(RC),successive projections algorithm(SPA),stepwise,competitive adaptive reweighted sampling(CARS),and uninformative variable elimination(UVE)were used to select the optimal wavelengths to optimize the MAS-PLS *** on the 18 optimal wavelengths(907.14,917.02,918.67,926.90,930.20,936.78,956.54,1004.28,1135.89,1211.56,1302.07,1367.94,1397.60,1488.31,1680.17,1683.49,1686.80 and 1695.10 nm)selected from MAS spectra by SPA,the MAS-SPA-PLS model was built with R_(P) of 0.920,RMSEP of 0.056 mg/g and RPD of 3.220,which was similar to the MAS-PLS *** overall study indicated that hyperspectral imaging in the 900-1700 nm range combined with PLS and SPA could be used to predict the IMP content in chicken flesh.

关键词： near-infrared hyperspectral imaging chicken inosinic acid partial least squares successive projections algorithm

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Near Infrared Spectroscopy Detection and Quantification of Herbal Medicines Adulterated with Sibutramine

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JOURNAL OF FORENSIC SCIENCES 2015年第5期60卷 1199-1205页

作者： da Silva, Neirivaldo Cavalcante Honorato, Ricardo Saldanha Pimentel, Maria Fernanda Garrigues, Salvador Luisa Cervera, Maria de la Guardia, Miguel Univ Fed Pernambuco Dept Quim Fundamental Recife PE Brazil Superintendencia Reg Pernambuco Dept Policia Fed BR-50030230 Recife PE Brazil Univ Fed Pernambuco Dept Engn Quim Recife PE Brazil Univ Valencia Dept Analyt Chem Valencia Spain

There is an increasing demand for herbal medicines in weight loss treatment. Some synthetic chemicals, such as sibutramine (SB), have been detected as adulterants in herbal formulations. In this study, two strategies using near infrared (NIR) spectroscopy have been developed to evaluate potential adulteration of herbal medicines with SB: a qualitative screening approach and a quantitative methodology based on multivariate calibration. Samples were composed by products commercialized as herbal medicines, as well as by laboratory adulterated samples. Spectra were obtained in the range of 14,000-4000 per cm. Using PLS-DA, a correct classification of 100% was achieved for the external validation set. In the quantitative approach, the root mean squares error of prediction (RMSEP), for both PLS and MLR models, was 0.2%w/w. The results prove the potential of NIR spectroscopy and multivariate calibration in quantifying sibutramine in adulterated herbal medicines samples.

关键词： forensic science herbal medicines sibutramine near infrared partial least squares discriminant analysis partial least squares regression multiple linear regression successive projections algorithm

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A digital image-based traceability tool of the geographical origins of Argentine propolis

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MICROCHEMICAL JOURNAL 2016年 128卷 62-67页

作者： Dario Pierini, Gaston Sousa Fernandes, David Douglas Goncalves Dias Diniz, Paulo Henrique Ugulino de Araujo, Mario Cesar Susana Di Nezio, Maria Eugenia Centurion, Maria Univ Nacl Sur INQUISUR UNS CONICET Dept Quim Av Alem 1253 B8000CPB Bahia Blanca Buenos Aires Argentina Univ Fed Paraiba Lab Automacao & Instrumentacao Quim Anal Quimiome Dept Quim POB 5093 BR-58051970 Joao Pessoa PB Brazil Univ Estadual Paraiba Ctr Ciencias & Tecnol Dept Quim BR-58429500 Campina Grande PB Brazil

Propolis is a hive product prepared by honeybees (Apis mellifera L.) widely used in pharmaceutical and food preparations that plays beneficial roles beyond basic nutrition and therapeutic properties. These benefits are related with its quality, which depends on various factors, such as geographical origin, botanical sources, collecting seasons, races of honeybees, climatic conditions and also the method of harvest In this sense, it would be helpful the implementation of a simple, fast and reliable analytical methodology for quality monitoring of propolis samples as a traceability tool of its geographical origin. Thus, this work proposes the use of digital images and chemometrics for the classification of raw propolis from six different geographical origins of the Buenos Aires Province, Argentina. For this purpose, different combinations between a color model (Grayscale, RGB and HSI) and a multivariate classifier (PCA-LDA, SIMCA, kNN, PLS-DA and SPA-LDA) were tested. The best analytical performance was achieved by SPA-LDA using Intensity histograms, classifying correctly a 100% of the samples in both training and test sets, taking in account the 27 variables selected by SPA. As a consequence, the proposed methodology serves to support local apiculturists, guaranteeing the offer of products with,a clear indication of geographical origin, and enhancing regional capabilities. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Propolis Classification Digital images Geographical origin successive projections algorithm Linear Discriminant analysis

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