Non-destructive assessment of quality traits in apples and pears using near infrared spectroscopy and chemometrics

Abstract The objective of this study was to evaluate the performance of a handheld NIR spectrometer for non-destructive quality analysis of apples and pears produced in the Brazilian Semi-arid region. NIR spectra were acquired with a portable spectrometer in the wavelength range of 750–1065 nm and reference analyses of dry matter content (DMC) and soluble solids content (SSC) were measured weekly during 10 weeks of storage at 0.5 °C. Spectra were pre-processed with standard normal variate and used to develop DMC and SSC models using partial least squares regression with full cross-validation. The models were validated using data not included in the calibration. Satisfactory prediction results were obtained for SSC in apples (R² = 0.58) and pears (R² = 0.55), and for DMC in apples (R² = 0.55) and pears (R² = 0.65). All prediction models showed a relative root mean square error of prediction lower than 8%. These findings indicate that the NIR spectrometer is a promising tool to be used for a rapid and non-destructive determination of internal quality traits in apples and pears.

摘要
本研究旨在评估一款手持式近红外（Near-Infrared, NIR）光谱仪，用于巴西半干旱产区苹果与梨的无损品质分析。在750–1065 nm的波长范围内，采用该便携式近红外光谱仪采集样品的近红外光谱；于0.5 ℃条件下储存10周，每周对样品的干物质含量（Dry Matter Content, DMC）与可溶性固形物含量（Soluble Solids Content, SSC）开展标准参考检测。光谱经标准正态变量变换（Standard Normal Variate, SNV）预处理后，采用结合全交叉验证的偏最小二乘回归（Partial Least Squares Regression, PLSR）构建DMC与SSC定量预测模型，并使用未参与校准建模的独立数据集对所建模型进行验证。苹果的SSC（决定系数R²=0.58）、梨的SSC（R²=0.55），以及苹果的DMC（R²=0.55）、梨的DMC（R²=0.65）均取得了令人满意的预测效果。所有预测模型的相对预测均方根误差均低于8%。本研究结果表明，近红外光谱仪可作为一种极具潜力的工具，用于快速无损测定苹果与梨的内部品质指标。