The potential of predicting maturity using total soluble solids (TSS) and identifying organic from inorganic pineapple fruits based on near-infrared (NIR) spectra fingerprints would be beneficial to farmers and consumers alike. In this study, a portable NIR spectrometer and chemometric techniques were combined to simultaneously identify organically produced pineapple fruits from conventionally produced ones (thus organic and inorganic) and also predict total soluble solids. A total of 90 intact pineapple fruits were scanned with the NIR spectrometer while a digital refractometer was used to measure TSS from extracted pineapple juice. After attempting several preprocessing techniques, multivariate calibration models were built using principal component analysis (PCA), K-nearest neighbor (KNN), and linear discriminant analysis (LDA) to identify the classes (organic and conventional pineapple fruits) while partial least squares regression (PLSR) method was used to determine TSS of the fruits. Among the identification techniques, the MSC-PCA-LDA model accurately identified organic from conventionally produced fruits at 100% identification rate. For quantification of TSS, the MSC-PLSR model gave Rp = 0.851 and RMSEC = 0.950 °Brix, and Rc = 0.854 and RMSEP = 0.842 °Brix at 5 principal components in the calibration set and prediction set, respectively. The results generally indicated that portable NIR spectrometer coupled with the appropriate chemometric tools could be employed for rapid nondestructive examination of pineapple quality and also to detect pineapple fraud due to mislabeling of conventionally produced fruits as organic ones. This would be helpful to farmers, consumers, and quality control officers.

中文翻译：

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使用手持式NIR光谱仪同时鉴定和定量完整菠萝果实质量参数的可行性研究

利用总可溶性固形物（TSS）预测成熟度并基于近红外（NIR）光谱指纹图谱从无机菠萝果实中识别有机物的潜力对农民和消费者均有利。在这项研究中，便携式NIR光谱仪和化学计量学技术相结合，可以同时从传统生产的菠萝中鉴定有机生产的菠萝果实（因此是有机和无机），还可以预测总可溶性固形物。用近红外光谱仪扫描了总共90个完整的菠萝果实，同时使用数字折光仪测量提取的菠萝汁中的TSS。在尝试了几种预处理技术之后，使用主成分分析（PCA）K建立了多元校准模型。-近邻（KNN）和线性判别分析（LDA）来识别类别（有机和常规菠萝果实），而偏最小二乘回归（PLSR）方法用于确定果实的TSS。在识别技术中，MSC-PCA-LDA模型可以以100％的识别率从传统生产的水果中准确识别出有机物。为了对TSS进行定量，MSC-PLSR模型分别在校准集和预测集的5个主要成分上给出Rp = 0.851和RMSEC = 0.950°Brix，Rc = 0.854和RMSEP = 0.842°Brix。结果通常表明，便携式NIR光谱仪结合适当的化学计量工具可用于菠萝质量的快速非破坏性检查，还可以检测由于将常规生产的水果错误标记为有机水果而导致的菠萝欺诈。这将对农民，消费者和质量控制人员有所帮助。