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光谱仪（handheld NIR spectrometer）对巴西半干旱地区出产的苹果与梨进行无损品质分析的应用性能。研究在750–1065 nm波长范围内，采用便携式光谱仪采集样品的近红外光谱；并于0.5 ℃的储存环境下，在10周的储存周期内每周开展干物质含量（dry matter content, DMC）与可溶性固形物含量（soluble solids content, SSC）的标准参考检测。光谱经标准正态变量变换（standard normal variate）预处理后，采用结合全交叉验证的偏最小二乘回归构建DMC与SSC预测模型，模型验证采用未参与校准的独立数据集。结果显示，苹果SSC预测模型的决定系数R²为0.58、梨为0.55，苹果DMC预测模型的R²为0.55、梨为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应用前景的工具，用于快速无损测定苹果与梨的内部品质指标。

搜集汇总

背景与挑战

背景概述

该数据集包含使用便携式近红外光谱仪对巴西半干旱地区生产的苹果和梨进行无损品质分析的研究数据。研究通过光谱数据预测了干物质含量和可溶性固形物含量，模型预测结果显示出较高的准确性，表明该技术在水果品质快速检测中的应用潜力。