Mining hyperspectral data for non-destructive and rapid prediction of nitrite content in ham sausages

Accurate and rapid determination of nitrite contents is an important step for guaranteeing sausage quality. This study attempted to mine hyperspectral data in the range of 900-1700 nm for non-destructive and rapid prediction of nitrite contents in sausages. The average spectra of 156 samples were collected to relate to the measured nitrite values by partial least squares (PLS) regression. Optimal wavelengths were respectively selected by successive projections algorithm (SPA) and regression coefficients (RC) to simplify the PLS model. The results indicated that PLS model established with 15 optimal wavelengths (900.5 nm, 907.1 nm, 908.8 nm, 912.1 nm, 915.4 nm, 920.3 nm, 922.0 nm, 941.7 nm, 979.6 nm, 1083.2 nm, 1213.2 nm, 1353.0 nm, 1460.2 nm, 1595.6 nm and 1699.9 nm) selected by SPA had better performance with rC, rCV, rP of 0.92, 0.89, 0.89 and RMSEC, RMSECV, RMSEP of 0.41 mg/kg, 0.89 mg/kg, 0.49 mg/kg, respectively, for calibration set, cross-validation and prediction set. It was concluded that hyperspectral data could be mined by PLS & SPA for realizing the rapid evaluation of nitrite content in ham sausages. Keywords: hyperspectral data, ham sausage, non-destructive and rapid prediction, nitrite, partial least squares (PLS) DOI: 10.25165/j.ijabe.20211402.5407 Citation: Zhu Y D, He H J, Jiang S Q, Ma H J, Chen F S, Xu B C, et al. Mining hyperspectral data for non-destructive and rapid prediction of nitrite content in ham sausages. Int J Agric & Biol Eng, 2021; 14(2): 182–187.

[1]  Bingquan Chu,et al.  Determination of Total Polysaccharides and Total Flavonoids in Chrysanthemum morifolium Using Near-Infrared Hyperspectral Imaging and Multivariate Analysis , 2018, Molecules.

[2]  Alberto Horcada,et al.  Rapid determination of the fatty acid profile in pork dry-cured sausages by NIR spectroscopy , 2011 .

[3]  Hong-Ju He,et al.  Toward enhancement in prediction of Pseudomonas counts distribution in salmon fillets using NIR hyperspectral imaging , 2015 .

[4]  Daniel Cozzolino,et al.  A Review on the Application of Infrared Technologies to Determine and Monitor Composition and Other Quality Characteristics in Raw Fish, Fish Products, and Seafood , 2012 .

[5]  Di Wu,et al.  Rapid and non-destructive determination of drip loss and pH distribution in farmed Atlantic salmon (Salmo salar) fillets using visible and near-infrared (Vis-NIR) hyperspectral imaging. , 2014, Food chemistry.

[6]  O. Oluwafemi,et al.  Amperometry detection of nitrite in food samples using tetrasulfonated copper phthalocyanine modified glassy carbon electrode , 2018, Sensors and Actuators B: Chemical.

[7]  Pengcheng Nie,et al.  Application of Time Series Hyperspectral Imaging (TS-HSI) for Determining Water Distribution Within Beef and Spectral Kinetic Analysis During Dehydration , 2013, Food and Bioprocess Technology.

[8]  S. Hur,et al.  Changes in the mutagenicity of heterocyclic amines, nitrite, and N-nitroso compound in pork patties during in vitro human digestion , 2018, LWT.

[9]  Chao‐Hui Feng,et al.  Hyperspectral Imaging in Tandem with R Statistics and Image Processing for Detection and Visualization of pH in Japanese Big Sausages Under Different Storage Conditions. , 2018, Journal of food science.

[10]  Hong-Ju He,et al.  Selection of Informative Spectral Wavelength for Evaluating and Visualising Enterobacteriaceae Contamination of Salmon Flesh , 2015, Food Analytical Methods.

[11]  Della Riccia Giacomo,et al.  A multivariate regression model for detection of fumonisins content in maize from near infrared spectra. , 2013, Food chemistry.

[12]  Di Wu,et al.  Non-destructive and rapid analysis of moisture distribution in farmed Atlantic salmon (Salmo salar) fillets using visible and near-infrared hyperspectral imaging , 2013 .

[13]  Douglas Fernandes Barbin,et al.  Prediction of water and protein contents and quality classification of Spanish cooked ham using NIR hyperspectral imaging , 2013 .

[14]  M. C. U. Araújo,et al.  The successive projections algorithm for variable selection in spectroscopic multicomponent analysis , 2001 .

[15]  S. Oshita,et al.  Online monitoring of red meat color using hyperspectral imaging. , 2016, Meat science.

[16]  Ubonrat Siripatrawan,et al.  Hyperspectral imaging for rapid evaluation and visualization of quality deterioration index of vacuum packaged dry-cured sausages , 2018 .

[17]  Chu Zhang,et al.  Grading of Chinese Cantonese Sausage Using Hyperspectral Imaging Combined with Chemometric Methods , 2017, Sensors.

[18]  D. Majou,et al.  Mechanisms of the bactericidal effects of nitrate and nitrite in cured meats. , 2018, Meat science.

[19]  R. Compton,et al.  Detection and determination of nitrate and nitrite: a review. , 2001, Talanta.

[20]  Seung-Chul Yoon,et al.  Recent advancement in near infrared spectroscopy and hyperspectral imaging techniques for quality and safety assessment of agricultural and food products in the China Agricultural University , 2018, NIR news.

[21]  Yao-Ze Feng,et al.  Near-infrared hyperspectral imaging in tandem with partial least squares regression and genetic algorithm for non-destructive determination and visualization of Pseudomonas loads in chicken fillets. , 2013, Talanta.

[22]  Colm P. O'Donnell,et al.  Evaluation of Vis-NIR hyperspectral imaging as a process analytical tool to classify brined pork samples and predict brining salt concentration , 2019, Journal of Food Engineering.

[23]  Jun-Hu Cheng,et al.  Rapid Quantification Analysis and Visualization of Escherichia coli Loads in Grass Carp Fish Flesh by Hyperspectral Imaging Method , 2015, Food and Bioprocess Technology.

[24]  Di Wu,et al.  Rapid and real-time prediction of lactic acid bacteria (LAB) in farmed salmon flesh using near-infrared (NIR) hyperspectral imaging combined with chemometric analysis , 2014 .

[25]  Ma Hanjun,et al.  Quick assessment of chicken spoilage based on hyperspectral NIR spectra combined with partial least squares regression , 2021, International Journal of Agricultural and Biological Engineering.

[26]  Da-Wen Sun,et al.  Interpretation and rapid detection of secondary structure modification of actomyosin during frozen storage by near-infrared hyperspectral imaging , 2019, Journal of Food Engineering.

[27]  Yoshio Makino,et al.  Hyperspectral imaging and multispectral imaging as the novel techniques for detecting defects in raw and processed meat products: Current state-of-the-art research advances , 2018 .

[28]  Chao‐Hui Feng,et al.  Estimation of adenosine triphosphate content in ready-to-eat sausages with different storage days, using hyperspectral imaging coupled with R statistics. , 2018, Food chemistry.

[29]  E. Stefaniak,et al.  Spectrophotometric Assessment of the Differences Between Total Nitrate/Nitrite Contents in Peel and Flesh of Cucumbers , 2018, Food Analytical Methods.

[30]  Yidan Bao,et al.  Rapid prediction of moisture content of dehydrated prawns using online hyperspectral imaging system. , 2012, Analytica chimica acta.

[31]  A. Peirs,et al.  Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review , 2007 .

[32]  Yoshio Makino,et al.  Real-time prediction of pre-cooked Japanese sausage color with different storage days using hyperspectral imaging. , 2018, Journal of the science of food and agriculture.

[33]  Jun-Hu Cheng,et al.  Hyperspectral Imaging Sensing of Changes in Moisture Content and Color of Beef During Microwave Heating Process , 2018, Food Analytical Methods.

[34]  Z. Azimifar,et al.  Determination of Total Viable Count in Rainbow-Trout Fish Fillets Based on Hyperspectral Imaging System and Different Variable Selection and Extraction of Reference Data Methods , 2018, Food Analytical Methods.