What is it about?
This study focuses on developing a non-destructive method to predict the ripeness level of Mandarin orange (Citrus reticulata cv. Batu 55) using a combination of reflectance and fluorescence spectroscopy. Traditional methods for assessing fruit maturity, such as measuring soluble solids content (SSC) and acidity, are destructive and time-consuming. While color and size are often used as external indicators, they do not always accurately reflect internal fruit maturity. This study explores a spectroscopic approach to improve ripeness assessment accuracy. By analyzing the optical properties of Mandarin Batu 55 oranges at different maturity stages, researchers found that combining reflectance (Vis-NIR) and fluorescence spectroscopy improves the prediction model for ripeness. The best predictive model, developed using partial least square regression (PLSR), achieved a high correlation (R² = 0.91) and a lower error rate (RMSE = 2.46). The findings demonstrate that integrating reflectance and fluorescence spectra provides a more reliable method for determining fruit maturity, which could be applied in automated fruit sorting systems to enhance post-harvest quality control and reduce waste.
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Why is it important?
This research is important because it provides a non-destructive, accurate, and efficient method for determining the ripeness of Mandarin orange (Citrus reticulata cv. Batu 55), which is crucial for post-harvest management, fruit sorting, and quality control. Traditionally, fruit ripeness is assessed using destructive methods such as measuring soluble solids content (SSC) and acidity, or through visual inspection based on skin color and size. However, these conventional methods are time-consuming, inconsistent, and often unreliable, especially for citrus varieties like Batu 55, where skin color changes minimally during ripening. The study’s approach—using reflectance and fluorescence spectroscopy—offers a more objective and efficient alternative. By integrating spectroscopic techniques, this research enhances ripeness prediction accuracy, which is critical for farmers, traders, and food industries to ensure that only optimally ripe fruits are harvested, transported, and marketed. The high correlation (R² = 0.91) and lower error rate (RMSE = 2.46) in the predictive model indicate that this method can be applied in automated fruit sorting systems, improving consistency, reducing food waste, and increasing market value. Additionally, this technique can contribute to smart agriculture innovations, where non-destructive sensing methods streamline quality control processes while maintaining fruit integrity. Overall, this research has significant implications for supply chain efficiency, consumer satisfaction, and the economic sustainability of citrus farming.
Perspectives
From my perspective, this research represents a significant advancement in post-harvest technology and precision agriculture, particularly for citrus farming. The integration of reflectance and fluorescence spectroscopy as a non-destructive method for predicting fruit ripeness is a game-changer for the citrus industry, offering an efficient, scalable, and objective alternative to traditional methods. Given that Mandarin Batu 55 is an economically valuable variety, improving ripeness assessment can directly impact market quality, pricing, and consumer satisfaction. What I find particularly compelling is the high accuracy (R² = 0.91) and low RMSE (2.46) achieved by combining spectral features, demonstrating the potential for real-world application in automated fruit sorting systems. However, a challenge that remains is scalability—while spectroscopy-based approaches show promise, cost and accessibility of such technology for small-scale farmers could be a limiting factor. Future research should focus on making this system more affordable and adaptable, possibly integrating it with portable or mobile-based devices for on-farm applications. Additionally, exploring machine learning techniques alongside spectroscopy could further refine prediction models and adapt the technology to different citrus varieties. This study serves as a foundation for future innovations in smart agriculture, reinforcing the importance of data-driven decision-making in fruit quality assessment. In the long run, this technology could help reduce post-harvest losses, optimize supply chains, and ensure that consumers receive the best-quality produce, ultimately benefiting both farmers and the food industry.
Zainuri Hanif
National Research and Innovation Agency (BRIN)
Read the Original
This page is a summary of: Mandarin orange (Citrus reticulata Blanco cv. Batu 55) ripeness level prediction using combination reflectance-fluorescence spectroscopy, Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy, December 2023, Elsevier,
DOI: 10.1016/j.saa.2023.123061.
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