Nondestructive intelligent detection of total viable count in pork based on miniaturized spectral sensor

Authors: ZUO, JIEWEN - China Agricultural University; PENG, YANKUN - China Agricultural University; LI, YONGYN - China Agricultural University; YIN, TIANZHEN - China Agricultural University; Chao, Kuanglin

Submitted to: Food Research International
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/27/2024
Publication Date: 12/28/2024
Citation: Zuo, J., Peng, Y., Li, Y., Yin, T., Chao, K. 2024. Nondestructive intelligent detection of total viable count in pork based on miniaturized spectral sensor. Food Research International. earticle: 197. https://doi.org/10.1016/j.foodres.2024.115184.
DOI: https://doi.org/10.1016/j.foodres.2024.115184

Interpretive Summary: Meat spoilage is the degradation of a meat’s chemical components including water, protein, and fat. Total viable count (TVC) is a measure of the total amount of living microorganisms present in a sample and is used as a key indicator of meat freshness. TVC values for meat increase as meat spoilage occurs. There is an urgent need to develop a portable technology for rapid and accurate detection of chemical food spoilage to reduce food safety risks, particularly during operations vulnerable to unwanted changes in environmental conditions such as product storage and transportation. In this study, we have developed a portable detection technology combined with machine learning techniques to predict the TVC in pork samples. The portable device acquires measurements of light reflected from the meat samples and automatically processes the measurements using a classification model to produce analysis results and can store and display the results for easy use by non-experts. Because image scanning and complex three-dimensional (3D) image processing are not required, the portable device can be used to analyze over 30 samples per minute. The results of this study will serve as an important reference which will benefit researchers who have interest in developing portable detection methods for rapid and accurate evaluation of meat freshness. This portable technology is adaptable to a variety of scenarios in meat processing operations, such as slaughtering lines, storage facilities, transportation trucks, and sales markets.

Technical Abstract: Changes in the freshness of pork due to microbial action during complex transportation and storage indicate an urgent need for in-situ, real-time monitoring techniques for chemical spoilage of meat. This study reported the use of a portable detection device based on a miniaturized visible/near-infrared spectrometer, combined with data noise reduction and machine learning methods, to predict the total viable count (TVC) in pork samples, a value that increases with as fresh meat undergoes spoilage. A rapid detection device for pork TVC was designed based on the miniaturized spectrometer; a spectral preprocessing method based on the resolution of the spectrometer was proposed; the effects of different preprocessing methods on the full-wavelength modeling effect were compared; and four different feature wavelength extraction algorithms were utilized for the feature wavelengths of pork TVC. The modeling effects of different simplified models were compared. The results showed that, for the full-wavelength models, resolution interval correction combined with standard normal variation was the most effective, with correlation coefficients of prediction set (RP), root mean square errors in prediction set (RMSEP), and relative percent deviation (RPD) of 0.918, 0.464 (lg CFU/g), and 2.508, respectively. From among the simplified models, interval random frog-partial least squares regression (iRF-PLSR) had the best predictive ability compared to the full-wavelength model, with the number of wavelengths used reduced by 85.45% and model performance improved with RP, RMSEP, and RPD values of 0.948, 0.392 (lg CFU/g) and 2.970, respectively. The combination of a rational spectral acquisition setup and a data processing methodology, the miniaturized spectrometer showed competitive results with the complex detection system in predicting meat TVC.