Agricultural Engineering Research Institute, Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran.
Laser and Plasma Research Institute, Shahid Beheshti University, Tehran, Iran.
Spectrochim Acta A Mol Biomol Spectrosc. 2019 Jan 5;206:552-557. doi: 10.1016/j.saa.2018.08.059. Epub 2018 Aug 29.
In this research, the feasibility of utilizing visible/near-infrared (Vis/NIR) spectroscopy as an optical non-destructive technique combined with both supervised and unsupervised pattern recognition methods was assessed for detection of Ectomyelois ceratoniae, carob moth, infestation in pomegranates during hidden activity of the larvae. To this end, some fruits were artificially contaminated to the carob moth larvae. Vis/NIR spectra of the blank samples and the contaminated pomegranates without and with external visual symptoms of larvae infestation were analyzed one and two weeks after contaminating the samples as three groups of "Healthy", "Unhealthy-A" and "Unhealthy-B", respectively. Principal component analysis (PCA) as unsupervised pattern recognition method was used to verify the possibility of clustering of the pomegranate samples into the three groups. Discriminant analysis (DA) based on PCA was also used as a powerful supervised pattern recognition method to classify the samples. The calibration models of linear, quadratic and Mahalanobis discriminant analyses were developed based on different spectral pre-processing techniques. The best PCA-DA model was obtained using Mahalanobis distance method and first derivative (D1) pre-processing. The total percentage of correctly classified samples with the best calibration model was 97.9%. The developed model could also predict unknown samples with total percentage of correctly classified samples of 90.6%. It was concluded that Vis/NIR spectroscopy combined with pattern recognition method of PCA-DA can be an appropriate and rapid technology for non-destructively screening the pomegranates for detection of carob moth infestation during hidden activity of the larvae.
在这项研究中，评估了可见/近红外（Vis/NIR）光谱作为一种光学非破坏性技术的可行性，该技术结合了有监督和无监督的模式识别方法，用于检测在幼虫隐蔽活动期间，石榴中角榴象（Ectomyelois ceratoniae）、角豆象的侵害。为此，一些石榴果实被人为地受到角豆象幼虫的污染。分析了空白样本以及无和有幼虫侵害外部视觉症状的污染石榴在污染后一和两周的 Vis/NIR 光谱，分别将它们分为三组：“健康”、“不健康-A”和“不健康-B”。主成分分析（PCA）作为无监督模式识别方法，用于验证石榴样本聚类为三组的可能性。基于 PCA 的判别分析（DA）也被用作一种强大的有监督模式识别方法来对样本进行分类。基于不同的光谱预处理技术，开发了线性、二次和马氏判别分析的校准模型。使用马氏距离法和一阶导数（D1）预处理，获得了最佳的 PCA-DA 模型。最佳校准模型的正确分类样品的总百分比为 97.9%。该模型还可以预测未知样品，正确分类样品的总百分比为 90.6%。研究得出结论，Vis/NIR 光谱结合 PCA-DA 的模式识别方法可以成为一种适当且快速的非破坏性技术，用于筛选石榴，以检测幼虫隐蔽活动期间角豆象的侵害。
