Abstract Visible and near infrared spectroscopy (VNIRS) (400−1000 nm) is a key emerging non-destructive technique for fruit quality assessment. This, because it is a unique method which allows rapid access to fruit pigments and chemical properties linked to fruit quality. In the present work, VNIRS has been used to predict the internal browning in ‘Keitt’ mangoes halves. The reference analysis was performed by cutting individual mango into halves and quantifying the extent of internal browning with a standardized color imaging (CI) cabinet as a browning index (BI). The CI provided a value for the “browning index” for each mango reflecting the presence and severity of internal browning. The data modelling involved both regression and classification analysis. The regression was performed to link the VNIR spectra with the BI values obtained from the internal color analysis. The classification analysis was performed for binary classification of mango into healthy or brown. Two different analysis techniques i.e. artificial neural network (ANN) and partial least square (PLS) were utilized. The study shows that VNIRS combined with ANN can classify mangoes as healthy or having internal brown with an accuracy of over 80 %. A robust and reliable classification system can potentially improve quality decisions through the mango supply chain, thereby reducing post-harvest losses.

中文翻译：

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使用人工神经网络分析支持的可见光和近红外光谱无损测量芒果内部褐变

摘要 可见光和近红外光谱 (VNIRS) (400−1000 nm) 是一种重要的新兴水果质量评估无损技术。这是因为它是一种独特的方法，可以快速获取与水果质量相关的水果色素和化学特性。在目前的工作中，VNIRS 已被用于预测“Keitt”芒果两半的内部褐变。参考分析是通过将单个芒果切成两半并使用标准化彩色成像 (CI) 柜量化内部褐变程度作为褐变指数 (BI) 进行的。CI 提供了每个芒果的“褐变指数”值，反映了内部褐变的存在和严重程度。数据建模涉及回归和分类分析。执行回归以将 VNIR 光谱与从内部颜色分析获得的 BI 值联系起来。对芒果进行了分类分析，将芒果分为健康或棕色。使用了两种不同的分析技术，即人工神经网络 (ANN) 和偏最小二乘法 (PLS)。研究表明，VNIRS 结合 ANN 可以将芒果分类为健康或内部褐色，准确率超过 80%。一个强大而可靠的分类系统可以潜在地通过芒果供应链改进质量决策，从而减少收获后的损失。利用人工神经网络（ANN）和偏最小二乘法（PLS）。研究表明，VNIRS 结合 ANN 可以将芒果分类为健康或内部褐色，准确率超过 80%。一个强大而可靠的分类系统可以潜在地通过芒果供应链改进质量决策，从而减少收获后的损失。利用人工神经网络（ANN）和偏最小二乘法（PLS）。研究表明，VNIRS 结合 ANN 可以将芒果分类为健康或内部褐色，准确率超过 80%。一个强大而可靠的分类系统可以潜在地通过芒果供应链改进质量决策，从而减少收获后的损失。