Parrini Silvia, Sirtori Francesco, Čandek-Potokar Marjeta, Charneca Rui, Crovetti Alessandro, Kušec Ivona Djurkin, Sanchez Elena González, Cebrian Mercedes Maria Izquierdo, Garcia Ana Haro, Karolyi Danijel, Lebret Benedicte, Ortiz Alberto, Panella-Riera Nuria, Petig Matthias, Jesus da Costa Pires Preciosa, Tejerina David, Razmaite Violeta, Aquilani Chiara, Bozzi Riccardo
Department of Agriculture, Food, Environment and Forestry, University of Florence, Piazzale delle Cascine 18, 50144, Florence, Italy.
Kmetijski Inštitut Slovenije, Hacquetova ulica 17, 1000, Ljubljana, Slovenia.
Sci Rep. 2023 May 15;13(1):7874. doi: 10.1038/s41598-023-34996-x.
The fatty acids profile has been playing a decisive role in recent years, thanks to technological, sensory and health demands from producers and consumers. The application of NIRS technique on fat tissues, could lead to more efficient, practical, and economical in the quality control. The study aim was to assess the accuracy of Fourier Transformed Near Infrared Spectroscopy technique to determine fatty acids composition in fat of 12 European local pig breeds. A total of 439 spectra of backfat were collected both in intact and minced tissue and then were analyzed using gas chromatographic analysis. Predictive equations were developed using the 80% of samples for the calibration, followed by full cross validation, and the remaining 20% for the external validation test. NIRS analysis of minced samples allowed a better response for fatty acid families, n6 PUFA, it is promising both for n3 PUFA quantification and for the screening (high, low value) of the major fatty acids. Intact fat prediction, although with a lower predictive ability, seems suitable for PUFA and n6 PUFA while for other families allows only a discrimination between high and low values.
近年来，由于生产者和消费者在技术、感官和健康方面的需求，脂肪酸谱发挥了决定性作用。近红外光谱技术在脂肪组织上的应用，可使质量控制更加高效、实用且经济。本研究旨在评估傅里叶变换近红外光谱技术测定12个欧洲本地猪品种脂肪中脂肪酸组成的准确性。共收集了439份完整组织和切碎组织的背膘光谱，然后使用气相色谱分析法进行分析。使用80%的样本进行校准，随后进行全交叉验证，其余20%用于外部验证测试，从而建立预测方程。对切碎样本的近红外光谱分析对脂肪酸家族、n6多不饱和脂肪酸（PUFA）有更好的响应，对n3 PUFA定量以及主要脂肪酸的筛选（高值、低值）都很有前景。完整脂肪预测虽然预测能力较低，但似乎适用于PUFA和n6 PUFA，而对于其他家族，仅能区分高值和低值。
