Work

Multi-source spectral data fusion combined with chemometrics for rapid analysis of key quality parameters in canine feed

Year: 2026

Type: article

Abstract: ABSTRACT With the rapid expansion of the pet food market, the quality, safety, and nutritional balance of pet feed have drawn increasing attention. Traditional analytical methods, although accurate, are often time‑consuming, involve complex procedures, and rely on chemical reagents, making them unsuitable for rapid quality control in modern production. To address these limitations, this study developed a rapid quantitative approach based on the fusion of portable near‑infrared (NIR) and attenuated total reflection mid‑infrared (ATR‑IR) spectroscopy combined with chemometrics for determining key nutritional components, including crude protein, crude fat, moisture, and lysine, in canine feed. Partial least squares regression (PLSR) models were established, and both low‑level and mid‑level data fusion strategies were evaluated. For crude protein and moisture, the NIR model alone provided the best predictive performance; for crude fat and lysine, the mid‑level fusion spectra achieved the highest accuracy. The proposed method is efficient, non‑destructive, and environmentally friendly, offering reliable technical support for rapid quality monitoring in pet feed production.

Source: Journal of Food Composition and Analysis

DOI: 10.1016/j.jfca.2026.109488

Authors: Li Dan, Liu Xiaolu, Guan Xin, Feng Yuchao, Tian Jing, Fan Xia

Institutions: Chinese Academy of Agricultural Sciences, Institute of Quality Standards and Testing Technology for Agro Products

Language: English

Topic: Spectroscopy and Chemometric Analyses

Subfield: Analytical Chemistry

Field: Chemistry

Domain: Physical Sciences

SDG: Zero hunger

Open Access status: hybrid

Estimated APC paid: $3,450