Non-destructive Assessment of Highbush Blueberry Fruit Maturity Parameters and Anthocyanins by Using a Visible/Near Infrared (vis/NIR) Spectroscopy Device: A Preliminary Approach

Ribera-Fonseca, Alejandra; Noferini, M.; Rombolà, Adamo Domenico

doi:10.4067/s0718-95162016005000014

Cited by 7 publications

(12 citation statements)

References 26 publications

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“…Nicoletta et al 40 also used a similar method to establish relevant models for predicting soluble solids, total anthocyanins, ascorbic acid, etc. It is also found the use of near-infrared spectroscopy to study the blueberry varieties and the firmness, 41 bruise, 42 maturity, 43 of fruit is becoming more and more perfect. Compared with near infrared spectroscopy technology, deep learning has just started in this field, and there are still many areas for improvement.…”

Section: Discussion Research Findingsmentioning

confidence: 99%

Non‐destructive detection of blueberry skin pigments and intrinsic fruit qualities based on deep learning

Yuan

Ouyang

et al. 2020

J Sci Food Agric

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BACKGROUND: This paper proposes a novel method to improve accuracy and efficiency in detecting the quality of blueberry fruit, taking advantage of deep learning in classification tasks. We first collected 'Tifblue' blueberries at seven different stages of maturity (10-70 days after full bloom) and measured the pigments of the blueberry skin and the total sugar and the total acid of the pulp. We then established a skin pigment contents prediction network (SPCPN), based on the correlation between the pigments and blueberry pictures, and also a fruit intrinsic qualities prediction network (FIQPN), based on the correlation between the pigments and fruit qualities. Finally, the SPCPN and FIQPN were consolidated into the blueberry quality parameters prediction network (BQPPN). RESULTS:The results showed that the anthocyanins in the blueberry skin were significantly correlated with the total sugar, total acid, and sugar / acid ratio of the fruit. After verification, the results also indicated that, for the prediction of anthocyanins, chlorophyll, and the anthocyanin / chlorophyll ratio, the SPCPN network model was found to achieve higher R 2 (RMSE) values of 0.969 (0.139), 0.955 (0.005), 0.967 (15.4), respectively. The FIQPN network model was also able to evaluate the value of total sugar (R 2 = 0.940, RMSE = 4.905), total acid (R 2 = 0.930, RMSE = 2.034), and the sugar / acid ratio (R2 = 0.973, RMSE = 0.580). CONCLUSION:The above results indicated the potential for utilizing deep learning technology to predict the quality indicators of blueberry before harvesting.

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Section: Discussion Research Findingsmentioning

confidence: 99%

Non‐destructive detection of blueberry skin pigments and intrinsic fruit qualities based on deep learning

Yuan

Ouyang

et al. 2020

J Sci Food Agric

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“…Ribera-Fonseca et al ( 71 ) evaluated the potential of a non-invasive tool to predict the fruit harvest date using maturity parameters. The device measures an index of absorbance difference (IAD), which has a high correlation with fruit quality parameters ( 71 ). This approach could not make use of any conventional algorithms; therefore, it relies on IAD values only.…”

Section: Applications Of Spectroscopic Techniquesmentioning

confidence: 99%

Recent advancements in NIR spectroscopy for assessing the quality and safety of horticultural products: A comprehensive review

et al. 2022

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The qualitative and quantitative evaluation of agricultural products has often been carried out using traditional, i.e., destructive, techniques. Due to their inherent disadvantages, non-destructive methods that use near-infrared spectroscopy (NIRS) coupled with chemometrics could be useful for evaluating various agricultural products. Advancements in computational power, machine learning, regression models, artificial neural networks (ANN), and other predictive tools have made their way into NIRS, improving its potential to be a feasible alternative to destructive measurements. Moreover, the incorporation of suitable preprocessing techniques and wavelength selection methods has arguably proven its practical feasibility. This review focuses on the various computation methods used for processing the spectral data collected and discusses the potential applications of NIRS for evaluating the quality and safety of agricultural products. The challenges associated with this technology are also discussed, as well as potential future perspectives. We conclude that NIRS is a potentially useful tool for the rapid assessment of the quality and safety of agricultural products.

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“…Harvest at optimal fruit maturity is therefore crucial for uninterrupted market supply with fresh and high-quality blueberry fruit [11]. Nowadays, visual estimation of blueberry peel color is the most important maturity index at harvest [12], since blueberries are known for their successive ripening [10]. The fruit is ripe once it has achieved a fully blue coloration [6], and this is the key quality parameter for visually attracting consumers.…”

Section: Introductionmentioning

confidence: 99%

“…So far, I AD measurements using a DA-meter have been found to be a useful tool for monitoring the ripening process in peaches [24,25], plums [22], grapevines [26,27], and apples [28][29][30], and predicting the harvest date in cherries [31]. As far as blueberry fruit is concerned, studies are scarce [12].…”

Section: Introductionmentioning

confidence: 99%

Correlation between Destructive and Non-Destructive Measurements of Highbush Blueberry (Vaccinium corymbosum L.) Fruit during Maturation

et al. 2023

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A relatively new, non-destructive, method for the assessment of optimal blueberry fruit maturity directly on the plant uses a DA-meter (delta absorbance) to measure chlorophyll absorbance (IAD). Here, ‘Aurora’ fruit quality parameters (peel color, fruit firmness), chemical composition (individual sugars, organic acids and phenolics) and the relative expression of anthocyanidin synthase (ANS) genes were examined at four different maturity stages (immature, light purple, darker purple, fully ripe). All observed parameters changed significantly during ripening and sometimes exhibited high correlation with IAD values, as R2 values ranged between 0.61 and 0.97. Peel lightness (L*), chroma (C*), fruit firmness and organic acids significantly decreased during ripening, while sugars, sugar/organic acid ratio, total phenolics and relative expression of ANS significantly increased. The current study is the first to determine IAD values during different maturity stages of ‘Aurora’ fruit using a DA-meter and to evaluate their correlation with the most commonly used quality parameters of ripe fruit, fruit chemical composition and relative expression of ANS.

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Non-destructive Assessment of Highbush Blueberry Fruit Maturity Parameters and Anthocyanins by Using a Visible/Near Infrared (vis/NIR) Spectroscopy Device: A Preliminary Approach

Cited by 7 publications

References 26 publications

Non‐destructive detection of blueberry skin pigments and intrinsic fruit qualities based on deep learning

Non‐destructive detection of blueberry skin pigments and intrinsic fruit qualities based on deep learning

Recent advancements in NIR spectroscopy for assessing the quality and safety of horticultural products: A comprehensive review

Correlation between Destructive and Non-Destructive Measurements of Highbush Blueberry (Vaccinium corymbosum L.) Fruit during Maturation

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