Panwar Roma, Lin Shin-Ping, Lin Shyh-Hsiang, Lin Jer-An, Wang Yu-Jen, Chuang Yung-Kun
Ph.D. Program in School of Nutrition and Health Sciences, College of Nutrition, Taipei Medical University, Taipei 11031, Taiwan.
School of Food Safety, College of Nutrition, Taipei Medical University, Taipei 11031, Taiwan.
Foods. 2025 Jun 12;14(12):2074. doi: 10.3390/foods14122074.
This study highlights the potential of Fourier-transform near-infrared (FT-NIR) spectroscopy for the on-site, nondestructive detection of spoilage caused by bacterial action in raw salmon () fillets. A stepwise multiple linear regression model with first-derivative spectrum transformation was combined with the standard normal variate and detrend preprocessing techniques. The model achieved correlation values of 0.97 in both the calibration and validation sample sets, with root mean square error values of 0.18 and 0.20 log CFU/mL, respectively. These accurate results reveal the precision of FT-NIR spectroscopy for assessing the spoilage caused by bacteria. The most informative wavelengths (885.27 nm, 1026.27 nm, 1039.93 nm, 1068.38 nm, 1257.55 nm, 1267.75 nm, and 1453.49 nm) related to the total bacterial count's identification were obtained. The innovative, cost-effective, and feasible approach outlined in this article is a promising methodology for enhancing the safety and quality standards of various fishery products.
本研究突出了傅里叶变换近红外（FT-NIR）光谱技术在现场无损检测生三文鱼（）鱼片细菌作用导致的腐败方面的潜力。将具有一阶导数光谱变换的逐步多元线性回归模型与标准正态变量和去趋势预处理技术相结合。该模型在校准和验证样本集中的相关值均达到0.97，均方根误差值分别为0.18和0.20 log CFU/mL。这些准确结果揭示了FT-NIR光谱技术在评估细菌导致的腐败方面的精度。获得了与总细菌数识别相关的最具信息性的波长（885.27 nm、1026.27 nm、1039.93 nm、1068.38 nm、1257.55 nm、1267.75 nm和1453.49 nm）。本文概述的创新、经济高效且可行的方法是提高各种渔业产品安全和质量标准的一种有前景的方法。
