Feasibility analysis of nondestructive detection of multiple parameters of highland barley by near-infrared spectroscopy.

IF 3.3 2区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Journal of the Science of Food and Agriculture Pub Date : 2025-03-28 DOI:10.1002/jsfa.14246

Linglei Li, Long Li, Guoyuan Gou, Lang Jia, Ruge Cao, Liya Liu, Litao Tong, Yonghu Zhang, Xiaogang Shen, Fengzhong Wang, Lili Wang

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引用次数: 0

Abstract

Background: Highland barley is widely regarded as a premium cereal grain due to its exceptional nutritional profile. This study employed near-infrared spectroscopy technology for the quantitative assessment of five critical parameters in highland barley: total starch, amylose, protein, β-glucan, and total phenols. To optimize spectral data processing, the most effective preprocessing method was identified among six options (standard normal transformation, multivariate scattering correction, normalization (Nor), detrend (DE), first derivative (FD), second derivative (SD)). Furthermore, feature wavelength selection algorithms, including competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), uninformative variable elimination, and least angle regression, were utilized to enhance the model's predictive accuracy.

Results: The commendable predictability for total starch was achieved through DE-SPA (R_p ² = 0.913, root mean square error of prediction (RMSEP) = 1.612). For amylose, Nor-CARS exhibited predictive performance (R_p ² = 0.925, RMSEP = 2.049). Protein showcased a creditable result by SD-SPA (R_p ² = 0.876, RMSEP = 0.710). β-Glucan achieved notable predictability through FD-CARS (R_p ² = 0.763, RMSEP = 0.328). Total phenols exhibited remarkable predictability using SD-SPA (R_p ² = 0.946, RMSEP = 0.130).

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来源期刊

Journal of the Science of Food and Agriculture 工程技术-农业综合

CiteScore

8.10

自引率

4.90%

发文量

634

审稿时长

3.1 months

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