Nofima, Ås, Norway.
Appl Spectrosc. 2021 Oct;75(10):1278-1287. doi: 10.1177/00037028211006150. Epub 2021 Apr 22.
Raman spectroscopy (RS) has for decades been considered a promising tool for food analysis, but widespread adoption has been held back by, e.g., high instrument costs and sampling limitations regarding heterogeneous samples. The aim of the present study was to use wide area RS in conjunction with surface scanning to overcome the obstacle of heterogeneity. Four different food matrices were scanned (intact and homogenized pork and by-products from salmon and poultry processing) and the bulk chemical parameters such as fat and protein content were estimated using partial least squares regression (PLSR). The performance of PLSR models from RS was compared with near-infrared spectroscopy (NIRS). Good to excellent results were obtained with PLSR models from RS for estimation of fat content in all food matrices (coefficient of determination for cross-validation (R) from 0.73 to 0.96 and root mean square error of cross-validation (RMSECV) from 0.43% to 2.06%). Poor to very good PLSR models were obtained for estimation of protein content in salmon and poultry by-product using RS (R from 0.56 to 0.92 and RMSECV from 0.85% to 0.94%). The performance of RS was similar to NIRS for all analyses. This work demonstrates the applicability of RS to analyze bulk composition in heterogeneous food matrices and paves way for future applications of RS in routine food analyses.
拉曼光谱（RS）几十年来一直被认为是一种很有前途的食品分析工具，但由于仪器成本高和对异质样品的采样限制等因素，其广泛应用受到了限制。本研究的目的是使用大面积 RS 结合表面扫描来克服异质的障碍。扫描了四种不同的食品基质（完整和均质的猪肉以及三文鱼和家禽加工的副产品），并使用偏最小二乘回归（PLSR）估算了脂肪和蛋白质等 bulk 化学参数含量。比较了 RS 和近红外光谱（NIRS）的 RS 拉曼光谱（RS）的性能。对于所有食品基质中的脂肪含量估计，PLSR 模型从 RS 得到了很好到优秀的结果（交叉验证的决定系数（R）从 0.73 到 0.96，交叉验证均方根误差（RMSECV）从 0.43%到 2.06%）。对于三文鱼和家禽副产品中的蛋白质含量估计，从 RS 得到了较差到非常好的 PLSR 模型（R 从 0.56 到 0.92，RMSECV 从 0.85%到 0.94%）。对于所有分析，RS 的性能与 NIRS 相似。这项工作证明了 RS 适用于分析异质食品基质中的 bulk 成分，并为 RS 在常规食品分析中的未来应用铺平了道路。
