Pankin Dmitrii, Povolotckaia Anastasia, Borisov Eugene, Povolotskiy Alexey, Borzenko Sergey, Gulyaev Anatoly, Gerasimenko Stanislav, Dorochov Alexey, Khamuev Viktor, Moskovskiy Maksim
Center for Optical and Laser Materials Research, St. Petersburg State University, Ulianovskaya 5, 198504 St. Petersburg, Russia.
Institute of Chemistry, St. Petersburg State University, Universitetskii pr. 26, 198504 St. Petersburg, Russia.
Foods. 2023 Sep 14;12(18):3426. doi: 10.3390/foods12183426.
Wheat has played an important role in human agriculture since ancient times. Increasing rates of processed wheat product fabrication require more and more laboratory studies of product quality. This, in turn, requires the use, in production and in field conditions, of sufficiently accurate, fast and relatively low-cost quality control methods, including the detection of fungal diseases. One of the most widespread fungal diseases of wheat in the world is ergot caused by the fungi genus . Optical methods are promising for this disease identification due to the relative ease of implementation and the possibility of performing fast analyses in large volumes. However, for application in practice, it is necessary to identify and substantiate characteristic spectral markers that make it possible to judge the sample contamination. In this regard, within the framework of this study, the methods of IR absorption spectroscopy in the MIR region and reflection spectroscopy in the UV-vis-NIR ranges, as well as luminescence spectroscopy, were used to study ergot-infected grains of winter wheat of the "Moskovskaya 56" cultivar. To justify the choice of the most specific spectral ranges, the methods of chemometric analysis with supervised classification, namely PCA-LDA and PCA-SVM, were applied. The possibility of separating infected grains according to the IR absorption, reflection spectra in the UV-vis-NIR ranges and visible luminescence spectra was tested.
自古以来,小麦在人类农业中就发挥着重要作用。加工小麦产品制造率的不断提高,需要对产品质量进行越来越多的实验室研究。这反过来又要求在生产和田间条件下使用足够准确、快速且成本相对较低的质量控制方法,包括检测真菌病害。世界上最普遍的小麦真菌病害之一是由麦角菌属真菌引起的麦角病。光学方法因其实施相对容易且能够对大量样本进行快速分析,有望用于这种病害的识别。然而,为了在实践中应用,有必要识别并证实能够判断样本污染情况的特征光谱标记。在本研究框架内,采用中红外区域的红外吸收光谱法、紫外-可见-近红外范围内的反射光谱法以及发光光谱法,对“莫斯科56号”冬小麦品种受麦角病感染的籽粒进行了研究。为了证明选择最具特异性光谱范围的合理性,应用了带有监督分类的化学计量分析方法,即主成分分析-线性判别分析(PCA-LDA)和主成分分析-支持向量机(PCA-SVM)。测试了根据红外吸收、紫外-可见-近红外范围内的反射光谱以及可见发光光谱分离受感染籽粒的可能性。