Analysis of Internal Quality Changes in Apples During Storage Using Near-Infrared Spectroscopy
Yande Liu1, Siwei Lv1, Xiaogang Jiang1
1Institute of Intelligent Mechanical and Electrical Equipment Innovation, East China Jiaotong University, Nanchang 330013, China.
Foods (Basel, Switzerland)
|April 26, 2025
Summary
Near-infrared spectroscopy accurately predicts apple quality changes during storage. Models determine optimal storage periods for different apple varieties and conditions, ensuring peak freshness before ripening.
Area of Science:
- Agricultural Science
- Food Science
- Spectroscopy
Background:
- Apple quality degrades during storage, impacting consumer value.
- Predicting optimal storage duration is crucial for maintaining apple quality.
Purpose of the Study:
- To evaluate internal apple quality changes during storage using near-infrared spectroscopy.
- To develop predictive models for optimal apple storage periods across varieties and conditions.
Main Methods:
- Near-infrared spectroscopy analyzed 384 apple samples (four varieties) over 7 weeks.
- Pretreatment methods (Normalization, MSC, SNV) and band selection (CARS, UVE) were applied.
- Partial Least Squares (PLS) regression models were constructed for prediction.
Main Results:
- Soluble Solid Content (SSC) and firmness changes varied with storage conditions (cold vs. room temperature).
- Optimal predictive models (Normalization-CARS-PLS) achieved high accuracy (Rp 0.904 for sugar at 1°C, Rp 0.823 for firmness at room temp).
Conclusions:
- Near-infrared spectroscopy effectively monitors internal apple quality during storage.
- Predictive models can guide optimal storage periods for different apple varieties, minimizing post-harvest losses.
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