Chen Jiemei, Fu Chunli, Pan Tao
Department of Biological Engineering, Jinan University, Huangpu Road West 601, Tianhe District, Guangzhou 510632, China.
Department of Optoelectronic Engineering, Jinan University, Huangpu Road West 601, Tianhe District, Guangzhou 510632, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2022 Sep 5;277:121291. doi: 10.1016/j.saa.2022.121291. Epub 2022 Apr 25.
The identification of soy sauce brands can avoid adulteration and fraud, which is meaningful for food safety screening. Using visible and near-infrared (Vis-NIR) spectroscopy combined with k-nearest neighbor (kNN), the four-category discriminant models of soy sauce brands were established. The soy sauce of three brands (identification) and the other ten brands (interference) were collected, and a total of four categories of samples were obtained. The spectral datasets of two measurement modals (1 mm, 10 mm) were obtained. Based on moving-window (MW) waveband screening and wavelength step-by-step phase-out (WSP), the MW-WSP-kNN algorithm was proposed and applied to the wavelength optimization for the four-category discriminant analysis. Using calibration-prediction-validation experiment design, various high accuracy models with a small number of wavelengths located in NIR region were determined. In the independent validation, for the 1 mm measurement modal, the selected thirty-five dual-wavelength models and one three-wavelength model were located in NIR combined and overtone frequency regions respectively, all achieved 100% total recognition accuracy rate (RAR); for the 10 mm measurement modal, the selected seven three-wavelength models located in NIR overtone frequency region all reached more than 96.8% RAR, and the optimal RAR was 97.8%. The results showed the feasibility of small number of wavelengths' NIR spectroscopy applied to multi-category discriminant of soy sauce brands, with the advantages of rapid, simple and miniaturized. The proposed various small number of wavelengths' models provided a valuable reference for the design of small dedicated spectrometer with different measurement modals. The integrated optimization method and wavelength selection strategy here are also expected to be applied to other fields.
酱油品牌的鉴别可以避免掺假和欺诈行为，这对食品安全筛查具有重要意义。利用可见近红外（Vis-NIR）光谱结合k近邻（kNN）算法，建立了酱油品牌的四类判别模型。收集了三个品牌（待鉴别）的酱油和其他十个品牌（干扰）的酱油，共得到四类样品。获得了两种测量模式（1毫米、10毫米）的光谱数据集。基于移动窗口（MW）波段筛选和波长逐步淘汰（WSP），提出了MW-WSP-kNN算法并将其应用于四类判别分析的波长优化。采用校准-预测-验证实验设计，确定了各种位于近红外区域且波长数量较少的高精度模型。在独立验证中，对于1毫米测量模式，所选的35个双波长模型和1个三波长模型分别位于近红外组合频率区域和泛频区域，总识别准确率（RAR）均达到100%；对于10毫米测量模式，所选的7个位于近红外泛频区域的三波长模型的RAR均超过96.8%，最佳RAR为97.8%。结果表明，少量波长的近红外光谱应用于酱油品牌多类别判别具有可行性，具有快速、简便和小型化的优点。所提出的各种少量波长模型为不同测量模式的小型专用光谱仪设计提供了有价值的参考。这里的综合优化方法和波长选择策略也有望应用于其他领域。
