Xin Peichen, Liu Yun, Yang Lufei, Yan Haoran, Feng Shuai, Zheng Decong
College of Agricultural Engineering, Shanxi Agricultural University, Taigu, Jinzhong 030801, China.
Dryland Farm Machinery Key Technology and Equipment Key Laboratory of Shanxi Province, Taigu, Jinzhong 030801, China.
Foods. 2024 Aug 17;13(16):2576. doi: 10.3390/foods13162576.
For buckwheat, the optimal harvest period is difficult to determine-too early or too late a harvest affects the nutritional quality of buckwheat. In this paper, physical and chemical tests are combined with a method using near-infrared spectroscopy nondestructive testing technology to study buckwheat harvest and determine the optimal harvest period. Physical and chemical tests to determine the growth cycle were performed at 83 days, 90 days, 93 days, 96 days, 99 days, and 102 days, in which the buckwheat grain starch, fat, protein, total flavonoid, and total phenol contents were assessed. Spectral images of buckwheat in six different harvest periods were collected using a near-infrared spectral imaging system. Four preprocessing methods (SNV, S-G, DWT, and the normaliz function) and three dimensionality reduction algorithms (IVSO, VCPA, VISSA) were used to process the raw buckwheat spectral data, and the full and eigen spectra were established as a random forest (RF). Random forest (RF) and Least Squares Support Vector Machine (LS-SVM) classification models were used to determine the full and eigen spectra, respectively, and the optimal model for the buckwheat single harvest period was determined and validated. Through physical and chemical tests, it was concluded that the 90-day harvest buckwheat grain protein, fat, and starch contents were the highest, and that the total flavonoid and total phenolic contents were also high. The SNV preprocessing method was the most effective, and the feature bands extracted using the IVSO algorithm were more representative. The IVSO-RF model was the best discriminative model for the classification of buckwheat in different harvest periods, with the correct rates of the training and prediction sets reaching 100% and 96.67%, respectively. When applying the IVSO-RF model to the buckwheat single harvest period to verify the classification, the correct rate of the training set for each harvest period reached 96%, and that of the prediction set reached 100%. Near-infrared spectroscopy combined with the IVSO-RF modeling method for buckwheat harvest period detection is a rapid, nondestructive classification method. When this was combined with physical and chemical analyses, it was determined that a growth cycle of 90 days is the best harvest period for buckwheat. The results of this study can not only improve the quality of buckwheat crops but also be applied to other crops to determine their optimal harvest period.
对于荞麦而言，最佳收获期难以确定——收获过早或过晚都会影响荞麦的营养品质。本文将理化测试与近红外光谱无损检测技术相结合，研究荞麦收获情况并确定最佳收获期。在83天、90天、93天、96天、99天和102天进行了确定生长周期的理化测试，评估了荞麦籽粒中的淀粉、脂肪、蛋白质、总黄酮和总酚含量。使用近红外光谱成像系统采集了六个不同收获期的荞麦光谱图像。采用四种预处理方法（标准正态变量变换、Savitzky-Golay平滑滤波、离散小波变换和归一化函数）和三种降维算法（改进的可变空间搜索算法、变量组合群体分析、变量重要性得分变量选择算法）对原始荞麦光谱数据进行处理，并将全谱和特征光谱建立为随机森林（RF）。分别使用随机森林（RF）和最小二乘支持向量机（LS-SVM）分类模型对全谱和特征光谱进行判别，确定并验证了荞麦单收获期的最佳模型。通过理化测试得出，90天收获的荞麦籽粒蛋白质、脂肪和淀粉含量最高，总黄酮和总酚含量也较高。标准正态变量变换预处理方法最为有效，使用改进的可变空间搜索算法提取的特征波段更具代表性。改进的可变空间搜索算法-随机森林模型是不同收获期荞麦分类的最佳判别模型，训练集和预测集的正确率分别达到100%和96.67%。将改进的可变空间搜索算法-随机森林模型应用于荞麦单收获期分类验证时，各收获期训练集的正确率达到96%，预测集的正确率达到100%。近红外光谱结合改进的可变空间搜索算法-随机森林建模方法用于荞麦收获期检测是一种快速、无损的分类方法。当将其与理化分析相结合时，确定90天的生长周期是荞麦的最佳收获期。本研究结果不仅可以提高荞麦作物的品质，还可应用于其他作物以确定其最佳收获期。
