Seasonal variation of antioxidant bioactive compounds in southern highbush blueberry leaves and non-destructive quality prediction in situ by a portable near-infrared spectrometer

Qi, Jianbo; Sun, Shuai; Zhang, Liuqiang; Zhu, Yilin; Zhou, Huiji; Gan, Xiaona; Li, Bo; Chen, Yiling; Li, Wei; Li, Tingzhao; Li, Yiming; Dong, Gangqiang; Zeng, Qilong

doi:10.1016/j.foodchem.2024.139925

Food Chemistry

2024

DOI: 10.1016/j.foodchem.2024.139925

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Seasonal variation of antioxidant bioactive compounds in southern highbush blueberry leaves and non-destructive quality prediction in situ by a portable near-infrared spectrometer

Jianbo Qi,

Shuai Sun,

Liuqiang Zhang

et al.

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Cited by 3 publications

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Nondestructively Determining Soluble Solids Content of Blueberries Using Reflection Hyperspectral Imaging Technique

Qiu,

Chen,

et al. 2024

Agronomy

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Effectively detecting the quality of blueberries is crucial for ensuring that high-quality products are supplied to the fresh market. This study developed a nondestructive method for determining the soluble solids content (SSC) of blueberry fruit by using a near-infrared hyperspectral imaging technique. The reflection hyperspectral images in the 900–1700 nm waveband range were collected from 480 fresh blueberry samples. An image analysis pipeline was developed to extract the spectrums of blueberries from the hyperspectral images. A regression model for quantifying SSC values was successfully established based on the full range of wavebands, achieving the highest RP2 of 0.8655 and the lowest RMSEP value of 0.4431 °Brix. Furthermore, three variable selection methods, namely the Successive Projections Algorithm (SPA), interval PLS (iPLS), and Genetic Algorithm (GA), were utilized to identify the feature wavebands for modeling. The models calibrated from feature wavebands generated an RMSEP of 0.4643 °Brix, 0.4791 °Brix, and 0.4764 °Brix, as well as the RP2 of 0.8507, 0.8397, and 0.8420 for SPA, iPLS, and GA, respectively. Furthermore, a pseudo-color distribution diagram of the SSC values within blueberries was successfully generated based on established models. This study demonstrated a novel approach for blueberry quality detection and inspection by jointly using hyperspectral imaging and machine learning methodologies. It can serve as a valuable reference for the development of grading equipment systems and portable testing devices for fruit quality assurance.

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Nondestructively Determining Soluble Solids Content of Blueberries Using Reflection Hyperspectral Imaging Technique

Qiu,

Chen,

et al. 2024

Agronomy

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Nondestructive Detection of Litchi Stem Borers Using Multi-Sensor Data Fusion

Zhao,

Xu,

et al. 2024

Agronomy

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To enhance lychee quality assessment and address inconsistencies in post-harvest pest detection, this study presents a multi-source fusion approach combining hyperspectral imaging, X-ray imaging, and visible/near-infrared (Vis/NIR) spectroscopy. Traditional single-sensor methods are limited in detecting pest damage, particularly in lychees with complex skins, as they often fail to capture both external and internal fruit characteristics. By integrating multiple sensors, our approach overcomes these limitations, offering a more accurate and robust detection system. Significant differences were observed between pest-free and infested lychees. Pest-free lychees exhibited higher hardness, soluble sugars (11% higher in flesh, 7% higher in peel), vitamin C (50% higher in flesh, 2% higher in peel), polyphenols, anthocyanins, and ORAC values (26%, 9%, and 14% higher, respectively). The Vis/NIR data processed with SG+SNV+CARS yielded a partial least squares regression (PLSR) model with an R2 of 0.82, an RMSE of 0.18, and accuracy of 89.22%. The hyperspectral model, using SG+MSC+SPA, achieved an R2 of 0.69, an RMSE of 0.23, and 81.74% accuracy, while the X-ray method with support vector regression (SVR) reached an R2 of 0.69, an RMSE of 0.22, and 76.25% accuracy. Through feature-level fusion, Recursive Feature Elimination with Cross-Validation (RFECV), and dimensionality reduction using PCA, we optimized hyperparameters and developed a Random Forest model. This model achieved 92.39% accuracy in pest detection, outperforming the individual methods by 3.17%, 10.25%, and 16.14%, respectively. The multi-source fusion approach also improved the overall accuracy by 4.79%, highlighting the critical role of sensor fusion in enhancing pest detection and supporting the development of automated non-destructive systems for lychee stem borer detection.

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Selection of Grape Varieties for Canned Processing Based on Peeling Performance, Sensory Quality, and Storage Stability

Feng,

Wang,

Chen

et al. 2024

Sustainability

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The integration of grapes into canned food processing not only effectively extends their shelf life but also preserves their rich nutrition and delightful flavor. This marks a significant advancement toward value-added products and sustainability in the grape industry. This study aims to evaluate the appropriateness of different grape varieties for canned grape production, with a focus on peeling characteristics, sensory quality, and storage properties. Our findings reveal that Kyoho, Takatsuma, and Zuijinxiang grapes stand out as promising candidates, characterized by their ease of peeling, minimal peeling loss, and efficient peeling time. Subsequently, a fuzzy mathematical sensory evaluation approach was employed to assess the taste, flavor, texture, appearance, and size of the peeled grapes from nine grape varieties. Notably, Kyoho (3.87), Takatsuma (3.70), and Zuijinxiang (3.57) grapes exhibited superior sensory scores compared with the other varieties. Regarding storage quality, after 180 days of storage, Kyoho grapes exhibited lower color difference by 12.97–23.50%, higher brittleness by 13.77–19.17%, total phenolic content by 15.73–29.29%, total flavonoid content by 28.54–39.31%, anthocyanin content by 23.81–35.66%, and stronger antioxidant capacity (IC50 DPPH: 24.42–69.55%; IC50 ABTS: 13.27–57.43%) compared with Takatsuma and Zuijinxiang grapes. This comprehensive assessment highlights Kyoho grapes as the most suitable variety for canned grape production, followed by Takatsuma and Zuijinxiang grapes. Their exceptional peeling characteristics, sensory qualities, and notable storage resilience position them as promising candidates for commercialization, presenting substantial potential for widespread acceptance among consumers.

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Seasonal variation of antioxidant bioactive compounds in southern highbush blueberry leaves and non-destructive quality prediction in situ by a portable near-infrared spectrometer

Cited by 3 publications

References 37 publications

Nondestructively Determining Soluble Solids Content of Blueberries Using Reflection Hyperspectral Imaging Technique

Nondestructively Determining Soluble Solids Content of Blueberries Using Reflection Hyperspectral Imaging Technique

Nondestructive Detection of Litchi Stem Borers Using Multi-Sensor Data Fusion

Selection of Grape Varieties for Canned Processing Based on Peeling Performance, Sensory Quality, and Storage Stability

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