Evaluation of avocado fruit maturity with a portable near-infrared spectrometer

Blakey, Robert J.

doi:10.1016/j.postharvbio.2016.06.016

Cited by 34 publications

(15 citation statements)

References 16 publications

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“…In addition, numerous studies on the identification of tobacco varieties [9], tobacco parts [10], tobacco grades [11][12][13], aroma styles [14], and planting areas [15,16] using NIR spectroscopy techniques have also been carried out. More specifically, the distinguishing ability of NIR spectroscopy has been evaluated to determine the maturity levels of avocados [17][18][19][20], tomatoes [21,22], lychees [23], pomegranates [24], dates [25], table grapes [26], watermelons [27], cotton bolls [28], truffles [29], white teas [30], and peaches [31]. Despite the increasing number of applications of NIR spectroscopy in crop and fruit quality assessments, there are still only a few reports regarding the use of this technique to classify the maturity levels of fresh tobacco leaves.…”

Section: Introductionmentioning

confidence: 99%

Discrimination of Fresh Tobacco Leaves with Different Maturity Levels by Near-Infrared (NIR) Spectroscopy and Deep Learning

Chen

Bin

Zou

et al. 2021

Journal of Analytical Methods in Chemistry

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The maturity affects the yield, quality, and economic value of tobacco leaves. Leaf maturity level discrimination is an important step in manual harvesting. However, the maturity judgment of fresh tobacco leaves by grower visual evaluation is subjective, which may lead to quality loss and low prices. Therefore, an objective and reliable discriminant technique for tobacco leaf maturity level based on near-infrared (NIR) spectroscopy combined with a deep learning approach of convolutional neural networks (CNNs) is proposed in this study. To assess the performance of the proposed maturity discriminant model, four conventional multiclass classification approaches—K-nearest neighbor (KNN), backpropagation neural network (BPNN), support vector machine (SVM), and extreme learning machine (ELM)—were employed for a comparative analysis of three categories (upper, middle, and lower position) of tobacco leaves. Experimental results showed that the CNN discriminant models were able to precisely classify the maturity level of tobacco leaves for the above three data sets with accuracies of 96.18%, 95.2%, and 97.31%, respectively. Moreover, the CNN models with strong feature extraction and learning ability were superior to the KNN, BPNN, SVM, and ELM models. Thus, NIR spectroscopy combined with CNN is a promising alternative to overcome the limitations of sensory assessment for tobacco leaf maturity level recognition. The development of a maturity-distinguishing model can provide an accurate, reliable, and scientific auxiliary means for tobacco leaf harvesting.

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Section: Introductionmentioning

confidence: 99%

Discrimination of Fresh Tobacco Leaves with Different Maturity Levels by Near-Infrared (NIR) Spectroscopy and Deep Learning

Chen

Bin

Zou

et al. 2021

Journal of Analytical Methods in Chemistry

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“…The storage cooling system in which fruits are transported also plays an important role and affects the physiochemical conditions (Astudillo-Ordóñez and Rodríguez, 2018 ). Non-destructive methods such near-infrared have also been used as the indicating parameters, but cannot give a precise maturity stage (Blakey, 2016 ). More data are needed to improve the accuracy of non-destructive methods, which will undoubtedly save time and money if they can be relied on.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Postharvest Maturity Indices of Commercial Avocado Varieties Grown at Various Elevations Along Lebanon's Coast

Salameh

Nacouzi²,

Lahoud³

et al. 2022

Front. Plant Sci.

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Avocado is known to be a climacteric fruit that must be harvested during the suitable physiological maturity stage to achieve the best edible characteristics and reach the required export standards. It is very hard to visually determine the optimum maturity phases in the different avocado varieties for harvesting, especially because of the limited changes in the external fruit morphology during the maturity phase and because the harvest season is extended throughout several months. Therefore, some laboratory analyses are very crucial to determine the best timing to harvest the fruit. The aim of this study was to evaluate the postharvest maturity indices over 3 harvest stages, mainly dry matter (DM), oil content (OC), fruit firmness, titratable acidity (TA), total soluble solid (TSS/Brix), and fruit weight in commercial avocado varieties in regard to different altitudes and agricultural practices. The varieties in this study were as follows: Hass, Lambhass, Ettinger, Fuerte, Pinkerton, Reed, and Horshim growing at different altitudes that ranged from 50 to 400 m in 7 different regions in Lebanon. Statistical comparison of maturity indices under different locations by variety and harvest stage was performed using one-way ANOVA as well as by principal component analysis (PCA). The results showed a high linear correlation between DM and OC over the different harvest stages. During the late harvest stage, the weight showed a negative correlation between fruit firmness and TSS. The minimum oil content % and dry matter % were recorded for Reed variety (8.2 DM and 9.7 OC%) and the highest oil content % and dry matter % were recorded for Fuerte variety (28.5 DM and 21.6 OC%). The data obtained during this study are used to achieve the best edible characteristics and export standards of commercial avocado varieties growing along the Lebanese coast.

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“…These studies have used multiple linear regression, principal component regression, and partial least squares regression models. In these, the authors used cross‐validation for calibration with R 2 in test values ranging from 0.4 23 to 0.98 20 . However, the use of this method to determine the DM content of the avocado fruit requires direct contact with the fruit.…”

Section: Introductionmentioning

confidence: 99%

Prediction of dry matter content of recently harvested ‘Hass’ avocado fruits using hyperspectral imaging

2020

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BACKGROUND‘Hass’ avocado consumption is increasing due to its organoleptic properties, so it is necessary to develop new technologies to guarantee export quality. Avocado fruits do not ripen on the tree, and the visual classification of its maturity is not accurate. The most commonly used fruit maturity indicator is the percentage of dry matter (DM). The aim of this research was to investigate a non‐destructive method with hyperspectral images to predict the percentage of DM of fruits across the spectral range of 400–1000 nm.RESULTSNo correlation between fruit weight and color with the percentage of DM was found in the study area. Cross‐validation efficiency of different data sources, including the spectrum extraction zone (the center, a line from the peduncle to the base, and the whole fruit) and the average of one or two fruit faces, was compared. Four linear regression models were compared. Data of the whole fruit and average of both sides per fruit using a support vector machine regression were selected for the prediction test. Following the cross‐validation concept, five sets of calibration and test data were selected and optimized for calibration. The best test prediction set comprised an R2 = 0.9, a root‐mean‐square error of 2.6 g kg−1 DM, a Pearson correlation of 0.95, and a ratio of prediction to deviation of 3.2.CONCLUSIONSThe results of the study indicate that hyperspectral images allow classifying export fruits and making harvesting decisions. © 2020 Society of Chemical Industry

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Evaluation of avocado fruit maturity with a portable near-infrared spectrometer

Cited by 34 publications

References 16 publications

Discrimination of Fresh Tobacco Leaves with Different Maturity Levels by Near-Infrared (NIR) Spectroscopy and Deep Learning

Discrimination of Fresh Tobacco Leaves with Different Maturity Levels by Near-Infrared (NIR) Spectroscopy and Deep Learning

Evaluation of Postharvest Maturity Indices of Commercial Avocado Varieties Grown at Various Elevations Along Lebanon's Coast

Prediction of dry matter content of recently harvested ‘Hass’ avocado fruits using hyperspectral imaging

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