Rapid determination of fat content: Advanced spectroscopic methods across diverse fish species

IF 5.6 1区农林科学 Q1 FOOD SCIENCE & TECHNOLOGY

Food Control Pub Date : 2024-10-15 DOI:10.1016/j.foodcont.2024.110948

Angeliki Doukaki , Lemonia-Christina Fengou , Anastasia Lytou , Maria-Konstantina Spyratou , Alexandra Nanou , Evangelia Krystalli , Katerina Pissaridi , George-John Nychas

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Abstract

The rapid estimation of fat content using spectroscopic-based sensors, irrespective of fish species (salmon, trout, sea bass, sea bream, tuna, cod, and mackerel), was primarily investigated in the present study. The fat content and fatty acid (FA) composition was quantified with reference methods. Fourier-Transform Infrared (FTIR) and Fourier-Transform Near-Infrared (FT-NIR) spectroscopy, along with multispectral imaging (MSI) instruments (both benchtop and portable), were evaluated for their ability to rapidly predict fat content in ground samples using data analysis. The performance of the PLS-R models was evaluated according to root mean square error (RMSE), coefficient of determination (R²) and residual prediction deviation (RPD). Also fish were classified as fat/low-fat and low/medium/high-fat using partial least squares discriminant analysis (PLS-DA). All fish species exhibited a consistent pattern of unsaturated fatty acids (UNFA) > monounsaturated fatty acids (MUFA) > polyunsaturated fatty acids (PUFA) > saturated fatty acids (SFA) and the range of fat content was from 22.8 (salmon) to 0.02 (cod), expressed in g/100 g of sample based on the reference methods. In terms of fat content prediction using rapid sensors the best performance indices of the test set were obtained from the benchtop-MSI (RMSE = 1.475, R² = 0.847, RPD = 2.581) and FT-NIR (RMSE = 1.638, R² = 0.855, RPD = 2.651) instruments, while FTIR and portable-MSI had scores of RMSE = 1.874, R² = 0.815, RPD = 2.309 and RMSE = 1.737, R² = 0.786, RPD = 2.191, respectively. All sensors discriminated fat from low-fat samples (accuracy = 100%). For low/medium/high fat the best results were achieved by benchtop-MSI (accuracy = 94.12%), followed by portable-MSI (accuracy = 88.24%) and FTIR (accuracy = 84.21%). Results were less satisfactory for the FT-NIR method (accuracy = 68.75%). This study demonstrates that vibrational and multispectral imaging spectroscopies, when coupled with data analysis, have potential to provide information about the nutritional quality (e.g., fat content) independent of the fish species. Such insights could contribute to the automation and potential digitalization of processes within the fields of food nutrition and the seafood industry.

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快速测定脂肪含量：适用于各种鱼类的先进光谱方法

本研究主要考察了使用基于光谱的传感器快速估算脂肪含量的方法，不论鱼的种类（鲑鱼、鳟鱼、鲈鱼、鲷鱼、金枪鱼、鳕鱼和鲭鱼）。脂肪含量和脂肪酸（FA）组成采用参考方法进行量化。傅立叶变换红外光谱（FTIR）和傅立叶变换近红外光谱（FT-NIR）以及多光谱成像（MSI）仪器（台式和便携式）利用数据分析对其快速预测地面样本中脂肪含量的能力进行了评估。根据均方根误差（RMSE）、判定系数（R2）和残差预测偏差（RPD）对 PLS-R 模型的性能进行了评估。此外，还利用偏最小二乘判别分析（PLS-DA）对鱼类进行了脂肪/低脂肪和低/中/高脂肪分类。所有鱼类物种的不饱和脂肪酸（UNFA）>；单不饱和脂肪酸（MUFA）>；多不饱和脂肪酸（PUFA）>；饱和脂肪酸（SFA）都表现出一致的模式，脂肪含量的范围从 22.8（鲑鱼）到 0.02（鳕鱼）不等，根据参考方法以 g/100 g 样品表示。就使用快速传感器预测脂肪含量而言，台式-MSI（RMSE = 1.475，R2 = 0.847，RPD = 2.581）和傅立叶变换-近红外（RMSE = 1.638, R2 = 0.855, RPD = 2.651），而傅立叶变换红外和便携式-MSI 的 RMSE 分别为 1.874, R2 = 0.815, RPD = 2.309 和 RMSE = 1.737, R2 = 0.786, RPD = 2.191。所有传感器都能区分脂肪和低脂样品（准确率 = 100%）。对于低脂/中脂/高脂，台式-MSI 的结果最好（准确率 = 94.12%），其次是便携式-MSI（准确率 = 88.24%）和傅立叶变换红外（准确率 = 84.21%）。傅立叶变换-近红外方法的结果不太令人满意（准确率 = 68.75%）。这项研究表明，振动和多光谱成像光谱与数据分析相结合，有可能提供与鱼的种类无关的营养质量信息（如脂肪含量）。这种见解有助于食品营养和海产品行业的自动化和潜在数字化进程。

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来源期刊

Food Control 工程技术-食品科技

CiteScore

12.20

自引率

6.70%

发文量

758

审稿时长

33 days

期刊介绍： Food Control is an international journal that provides essential information for those involved in food safety and process control. Food Control covers the below areas that relate to food process control or to food safety of human foods: • Microbial food safety and antimicrobial systems • Mycotoxins • Hazard analysis, HACCP and food safety objectives • Risk assessment, including microbial and chemical hazards • Quality assurance • Good manufacturing practices • Food process systems design and control • Food Packaging technology and materials in contact with foods • Rapid methods of analysis and detection, including sensor technology • Codes of practice, legislation and international harmonization • Consumer issues • Education, training and research needs. The scope of Food Control is comprehensive and includes original research papers, authoritative reviews, short communications, comment articles that report on new developments in food control, and position papers.