巧克力作为一种休闲食品，以其细腻的口感和独特的口味而广泛受到消费者的青睐。然而，近几年来关于巧克力掺假的报道不断涌入人们的视野。其中，以廉价淀粉掺假巧克力的手段最为常见。本文研究利用近红外光谱快速检测巧克力中掺假红薯淀粉和马铃薯淀粉的方法，采用主成分回归（principal component regression，PCR）和偏最小二乘法（partial least squares regression，PLS）建立校正模型，并对比了光谱区间、光谱预处理方式以及主因子数对模型的影响。结果显示，采用PLS建模，光谱采用一阶导数处理（7pts），光谱区间选择在7000~4200 cm-1，主因子数为8时，模型预测效果最佳。结果表明，模型的预测误差均方根RMSEP=1.7%，实际值与预测值相关系数RP2=0.9426。该模型对不同掺假比例样品的加样回收率为94.2%~105.6%，日内RSD为4.7%~8.9%，日间RSD为5.1%~11.3%。结果表明，近红外光谱技术可用于快速检测巧克力中掺假淀粉。

Chocolates are a popular snack due to their exquisite taste and unique flavor. Nevertheless, instances of chocolate adulteration are on the rise and have recently caught consumer attention, the most common being adulteration with low-cost starch. In this study, near-infrared (NIR) spectroscopy was used for the rapid detection of adulterate sweet potato or potato starch in chocolate. A correction model was established using principal component regression (PCR) and partial least squares (PLS). The effects of spectral range, spectral preprocessing method, and the number of parameters were studied. The result indicated that the highest prediction rate was achieved using PLS for modeling with eight parameters, within a spectral range of 7000 to 4200 cm-1 and first derivative (7 pts) pretreatment. The values for root mean square error of prediction (RMSEP) and the coefficient of determination (Rp2) obtained with this model were 1.7% and 0.9426, respectively. The recovery rates, intraday RSD and interday RSD of samples with varied adulterated proportions were 94.2% to 105.6%, 4.7% to 8.9%, and 5.1% to 11.3%, respectively. The results indicate that NIR spectroscopy can be used as a method to rapidly detect adulterate starch in chocolate.

乐山市科技计划项目（13GZD072）