College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, China.
School of Environment and Natural Resources, Ohio State University, Columbus, Ohio, USA.
J Food Sci. 2023 Nov;88(11):4602-4619. doi: 10.1111/1750-3841.16769. Epub 2023 Sep 27.
Blueberries are a nutritious and popular berry worldwide. The physical and chemical properties of blueberries constantly change through the cycle of the supply chain (from harvest to sale). The purpose of this study was to develop a rapid method for detecting the properties of packaged blueberries based on near-infrared (NIR) spectroscopy. NIR was applied to quantitatively determine the soluble solid content (SSC) of polyethylene (PE)-packaged blueberries. An orthogonal partial least squares discriminant analysis model was established to show the correlation between spectral data and the measured SSC. Multiplicative scattering correction, standard normal variable, Savitzky-Golay convolution first derivative, and normalization (Normalize) were used for spectra preprocessing. Uninformative variables elimination, competitive adaptive reweighted sampling, and iteratively retaining informative variables were jointly used for wavelength optimization. NIR-based SSC prediction models for unpacked blueberries and PE-packaged blueberries were developed using partial least squares (PLS). The prediction model for PE-packaged samples (R = 0.876, root mean square error of prediction [RMSEP] = 0.632) had less precision than the model for unpacked samples (R = 0.953, RMSEP = 0.611). To reduce the effect of PE, the back propagation (BP) neural network and PLS were combined into the BP-PLS algorithm based on the residual learning algorithm. The model of BP-PLS (R = 0.947, RMSEP = 0.414) was successfully developed to improve the prediction accuracy of SSC for PE-packaged blueberries. The results suggested a promising way of using the BP-PLS method in tandem with NIR for the rapid detection of the SSC of PE-packaged blueberries. PRACTICAL APPLICATION: Most of the NIR-based research used unpacked blueberries as samples, while the use of packaged blueberries would provide researchers with a better understanding of the crucial factors at different phases of the blueberry supply chain (from harvest to sale). To meet market demands and minimize losses, NIR spectroscopy has been proven to be a rapid and nondestructive method for the determination of the SSC of PE-packaged blueberries. This study provides an effective method for monitoring the properties of blueberries in the entire supply chain.
蓝莓是一种营养丰富且在全球范围内广受欢迎的浆果。蓝莓的物理和化学性质在供应链周期（从收获到销售）中不断变化。本研究旨在开发一种基于近红外（NIR）光谱的快速检测包装蓝莓特性的方法。NIR 用于定量测定聚乙烯（PE）包装蓝莓的可溶性固形物含量（SSC）。建立了正交偏最小二乘判别分析模型，以显示光谱数据与测量 SSC 之间的相关性。采用多元散射校正、标准正态变量、Savitzky-Golay 卷积一阶导数和归一化（Normalize）对光谱进行预处理。联合使用无信息变量消除、竞争自适应重加权采样和迭代保留信息变量对波长进行优化。使用偏最小二乘（PLS）为未包装蓝莓和 PE 包装蓝莓开发了基于 NIR 的 SSC 预测模型。PE 包装样品的预测模型（R = 0.876，预测均方根误差 [RMSEP] = 0.632）的精度低于未包装样品的模型（R = 0.953，RMSEP = 0.611）。为了降低 PE 的影响，基于残差学习算法，将反向传播（BP）神经网络和 PLS 结合到 BP-PLS 算法中。成功开发了 BP-PLS 模型（R = 0.947，RMSEP = 0.414），以提高 PE 包装蓝莓 SSC 的预测精度。结果表明，使用 BP-PLS 方法与 NIR 结合快速检测 PE 包装蓝莓 SSC 是一种很有前途的方法。
大多数基于 NIR 的研究都使用未包装的蓝莓作为样品，而使用包装的蓝莓将使研究人员更好地了解蓝莓供应链（从收获到销售）不同阶段的关键因素。为了满足市场需求并最大程度地减少损失，已经证明近红外光谱法是一种快速、无损的方法，可用于测定 PE 包装蓝莓的 SSC。本研究为监测整个供应链中蓝莓的特性提供了一种有效方法。
