Hyperspectral prediction of amino acid content in Yunling marbled beef

doi:10.14088/j.cnki.issn0439-8114.2024.07.020

Abstract

Abstract: A method for non-destructive and rapid determination of the amino acid content of Yunling marbled beef based on hyperspectral imaging technology combined with machine learning was introduced. Hyperspectral data were collected in the 400~1 000 nm and 900~2 500 nm bands for 100 groups of marbled beef from five grades of Yunling cattle. The JJG1064-2011 standard amino acid analyzer was used to measure the content of 17 amino acids in the sample. The first-order difference （1st Derivative, D1） was used for hyperspectral data preprocessing, and the Successive projection algorithm （SPA） was used for feature band extraction. Five methods including Decision trees （Decision trees）, Support vector machine （SVM）, Ridge regression （Ridge regression）, Partial least squares regression （PLSR） and Convolutional neural network （CNN） were used for predicting amino acid content. Experimental results showed that the CNN model combined with D1 preprocessing and SPA feature extraction performed best in predicting amino acid content, with mean squared error （MSE） of 0.010 3, mean absolute error （MAE） of 0.082 2, and the coefficient of determination （R²） of 0.898 5.

Key words: hyperspectral imaging technology, Yunling marbled beef, amino acid, predictive model

CLC Number:

O157.3

LUO Shuang, YANG Lin-nan, ZHANG Li-lian, PENG Lin, LI Pei-shan, GAO Lu-tao. Hyperspectral prediction of amino acid content in Yunling marbled beef[J]. HUBEI AGRICULTURAL SCIENCES, 2024, 63(7): 120-128.

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