Lu Yuzhen, Lu Renfu
Department of Agricultural and Biological Engineering, Mississippi State University, Starkville, MS 39762, USA.
United States Department of Agriculture Agricultural Research Service, East Lansing, MI 48824, USA.
Foods. 2021 May 14;10(5):1094. doi: 10.3390/foods10051094.
Pickling cucumbers are susceptible to chilling injury (CI) during postharvest refrigerated storage, which would result in quality degradation and economic loss. It is, thus, desirable to remove the defective fruit before they are marketed as fresh products or processed into pickled products. Chlorophyll fluorescence is sensitive to CI in green fruits, because exposure to chilling temperatures can induce detectable alterations in chlorophylls of tissues. This study evaluated the feasibility of using a dual-band chlorophyll fluorescence imaging (CFI) technique for detecting CI-affected pickling cucumbers. Chlorophyll fluorescence images at 675 nm and 750 nm were acquired from pickling cucumbers under the excitation of ultraviolet-blue light. The raw images were processed for vignetting corrections through bi-dimensional empirical mode decomposition and subsequent image reconstruction. The fluorescence images were effective for ascertaining CI-affected tissues, which appeared as dark areas in the images. Support vector machine models were developed for classifying pickling cucumbers into two or three classes using the features extracted from the fluorescence images. Fusing the features of fluorescence images at 675 nm and 750 nm resulted in overall accuracies of 96.9% and 91.2% for two-class (normal and injured) and three-class (normal, mildly and severely injured) classification, respectively, which are statistically significantly better than those obtained using the features at a single wavelength, especially for the three-class classification. Furthermore, a subset of features, selected based on the neighborhood component feature selection technique, achieved the highest accuracies of 97.4% and 91.3% for the two-class and three-class classification, respectively. This study demonstrated that dual-band CFI is an effective modality for CI detection in pickling cucumbers.
腌渍黄瓜在采后冷藏储存期间易受冷害(CI)影响,这会导致品质下降和经济损失。因此,在将有缺陷的果实作为新鲜产品销售或加工成腌渍产品之前将其去除是很有必要的。叶绿素荧光对绿色果实中的冷害敏感,因为暴露在低温下会导致组织中的叶绿素发生可检测到的变化。本研究评估了使用双波段叶绿素荧光成像(CFI)技术检测受冷害影响的腌渍黄瓜的可行性。在紫外蓝光激发下,从腌渍黄瓜获取675nm和750nm的叶绿素荧光图像。通过二维经验模态分解和随后的图像重建对原始图像进行渐晕校正处理。荧光图像对于确定受冷害影响的组织很有效,这些组织在图像中呈现为暗区。利用从荧光图像中提取的特征,开发了支持向量机模型,将腌渍黄瓜分为两类或三类。融合675nm和750nm荧光图像的特征,两类(正常和受损)和三类(正常、轻度和重度受损)分类的总体准确率分别为96.9%和91.2%,在统计学上显著优于使用单一波长特征获得的准确率,尤其是对于三类分类。此外,基于邻域成分特征选择技术选择的一组特征,两类和三类分类的最高准确率分别达到97.4%和91.3%。本研究表明,双波段CFI是检测腌渍黄瓜冷害的有效方法。