A three-wavelength spectral approach for automated detection of chilling damage and juice content in oranges

• A robust linear spectral index was developed for accurate identification of chilling damage in orange fruit. • Wavelengths at 520, 675, and 1710 nm were identified as key spectral markers associated with physiological chilling damage and juice content. • Orange juice content was reliably predicted using absorbance values at the three selected wavelengths. • A combined Vis/NIR spectroscopy and machine learning framework enabled early detection of chilling damage and non-destructive estimation of juice content in oranges. Early and accurate detection of chilling damage in citrus is essential for preserving fruit quality and reducing postharvest economic losses. This study presents a rapid, non-destructive approach for identifying early-stage chilling injury and predicting juice content in oranges by integrating visible–near infrared (Vis/NIR) spectroscopy with machine learning (ML). Spectral absorbance in the range of 400–2500 nm was collected from 200 field-grown oranges representing both sound (healthy) fruit and those exhibiting early chilling damage. The spectra were pre-processed using Diameter Normalization, Standard Normal Variate, Multiplicative Scatter Correction, and first- and second-order derivatives. Six ML classifiers such as Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Classifier, Random Forest, XGBoost, Gaussian Process classifier, and Extreme Learning Machine (ELM) were applied to differentiate sound oranges from chilling-damaged ones. Juice content was then estimated using six regression models, including Partial Least Squares Regression (PLS-R), Support Vector Regressor, Random Forest Regressor, XGBoost Regressor, Gaussian Process Regressor, and ELM Regressor. All models were trained and cross-validated on both full-spectra inputs and selected informative wavelengths sets. A multi-method wavelength importance analysis PLS VIP (Variable Importance in Projection) scores and Random Forest Gini/MSE metrics consistently identified three pivotal wavelengths 520, 675, and 1710 nm associated with chlorophyll degradation, pigment changes, and water/sugar dynamics. Remarkably, models using only these three wavelengths achieved performance comparable to full-spectra models: ELM reached 0.980 accuracy for chilling damage classification versus 0.990 for full-spectra PLS-DA and 0.871 R² for juice prediction versus 0.910 R² for the full-spectra Gaussian Process Regressor. Leveraging these wavelengths, we formulated a logistic regression index that provides transparent, computationally efficient chilling-damage probabilities highly aligned with ML predictions. This simplified spectral fingerprint enables real-time, on-site fruit quality screening while minimizing hardware complexity. Overall, the findings highlight that a concise set of informative wavelengths can effectively replace high-dimensional spectral models without compromising accuracy, offering a practical and scalable pathway for automated citrus quality assessment.