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 本研究旨在评估手持式近红外（Near Infrared, NIR）光谱仪对巴西半干旱地区出产的苹果与梨开展无损品质分析的性能。实验采用便携式光谱仪采集750~1065纳米波长范围内的近红外光谱，并在0.5℃条件下储存的10周内，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）进行对照分析。采用标准正态变量法对光谱进行预处理，结合全交叉验证的偏最小二乘回归法构建干物质含量与可溶性固形物含量预测模型，并使用未纳入校正集的数据对模型进行验证。结果显示，苹果的可溶性固形物含量（R²=0.58）、梨的可溶性固形物含量（R²=0.55）、苹果的干物质含量（R²=0.55）以及梨的干物质含量（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，该近红外光谱仪有望成为快速、无损测定苹果与梨内部品质指标的可靠工具。