Nondestructive detection of maturity of watermelon by spectral characteristic using NIR diffuse transmittance technique

Jie, Dengfei; Zhou, Wanhuai; Wei, Xuan

doi:10.1016/j.scienta.2019.108718

Cited by 30 publications

(22 citation statements)

References 31 publications

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“…Different granulation levels of samples were set from none to greater than 75% granulation, and designated A, B, C, D, and E. The classification accuracy represents the percentage of the samples classified correctly of all samples, and this was used to estimate model performance [44]. The accuracy rate of a model (ARM) was defined as follows [12]:…”

Section: Modeling Methods and Model Evaluationmentioning

confidence: 99%

“…In the confusion matrix, the ARM of each model represents the ratio as the sum of samples on the main diagonal to the total number of samples. According to the prediction set of samples (Table 4), the ARM values of LDA, PCA-SVM, and PCA-GRNN were 87.5%, 95.5%, and 95.5%, respectively, as calculated by Equation (12). Based on TP, TN, FP, and FN, predictive performance measures (CA, CS, and CSP) could be derived from Equations (13) to (15).…”

Section: Discriminant Analysis Of Models In Five Granulation Levelsmentioning

confidence: 99%

“…Visible and near-infrared transmittance spectroscopy has been widely applied to identify and estimate the internal quality attributes of fruit, by assessing soluble solids content (SSC) [11], maturity [12], and early decay [13]. However, there have been few studies of granulation of citrus fruit.…”

Section: Introductionmentioning

confidence: 99%

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Non-Destructive Identification and Estimation of Granulation in Honey Pomelo Using Visible and Near-Infrared Transmittance Spectroscopy Combined with Machine Vision Technology

Sun

2020

Applied Sciences

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Granulation is a physiological disorder of juice sacs in citrus fruit, causing juice sacs to become hard and dry and resulting in decreased internal quality of citrus fruit. Honey pomelo is a thick-skinned citrus fruit, and it is difficult to identify the extent of granulation by observation of the outer peel and fruit shape. In this study, a rapid and non-destructive testing method using visible and near-infrared transmittance spectroscopy combined with machine vision technology was applied to identify and estimate granulation inside fruit. A total of 600 samples in different growth periods was harvested, and fruit were divided into five classes according to five granulation levels. Spectral data were obtained for two ranges of 400–1100 nm and 900–1700 nm by visible and near-infrared transmittance spectroscopy. In addition, chemometrics were used to measure the chemical changes of soluble solid content (SSC), titratable acidity (TA), and moisture content (MC) caused by different granulation levels. Machine vision technology can rapidly estimate the external characteristics of samples and measure the physical changes in mass and volume caused by different granulation levels. Compared with using a single or traditional methods, the predictive performances of multi-category classification models (PCA-SVM and PCA-GRNN) (Table A1) were significantly enhanced. In particular, the model accuracy rate (ARM) was 99% for PCA-GRNN, with classification accuracy (CA), classification sensitivity (CS), and classification specificity (CSP) of 0.9950, 0.9750, and 0.9934, respectively. The results showed that this method has great potential for the identification and estimation of granulation. Multi-source data fusion and application of a multi-category classification model with the smallest number of input layers and acceptable high predictive performances are proposed for on-line applications. This method can be effectively used on-line for the non-destructive detection of fruits with granulation.

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Section: Modeling Methods and Model Evaluationmentioning

confidence: 99%

Section: Discriminant Analysis Of Models In Five Granulation Levelsmentioning

confidence: 99%

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Non-Destructive Identification and Estimation of Granulation in Honey Pomelo Using Visible and Near-Infrared Transmittance Spectroscopy Combined with Machine Vision Technology

Sun

2020

Applied Sciences

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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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“…Table S2: Applications of commercially available devices that may be applied for authentication of meat, meat products, and offal, Table S3: Applications of commercially available devices that may be applied for authentication of milk and milk products, Table S4: Applications of commercially available devices that may be applied for authentication of fish and seafood, Table S5: Applications of commercially available devices that may be applied for authentication of products of animal origin other than those presented in Tables S2–S4, Table S6: Applications of commercially available devices that may be applied for authentication of fresh and dried food products of plant origin, Table S7: Applications of commercially available devices that may be applied for authentication of processed food products of plant origin. References [ 157 , 158 , 159 , 160 , 161 , 162 , 163 , 164 , 165 , 166 , 167 , 168 , 169 , 170 , 171 , 172 , 173 , 174 , 175 , 176 , 177 , 178 , 179 , 180 , 181 , 182 , 183 , 184 , 185 , 186 , 187 , 188 , 189 , 190 , 191 , 192 , 193 , 194 , 195 , 196 , 197 , 198 , 199 , 200 , …”

mentioning

confidence: 99%

Handheld Devices for Food Authentication and Their Applications: A Review

Müller-Maatsch

Ruth

2021

Foods

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This review summarises miniaturised technologies, commercially available devices, and device applications for food authentication or measurement of features that could potentially be used for authentication. We first focus on the handheld technologies and their generic characteristics: (1) technology types available, (2) their design and mode of operation, and (3) data handling and output systems. Subsequently, applications are reviewed according to commodity type for products of animal and plant origin. The 150 applications of commercial, handheld devices involve a large variety of technologies, such as various types of spectroscopy, imaging, and sensor arrays. The majority of applications, ~60%, aim at food products of plant origin. The technologies are not specifically aimed at certain commodities or product features, and no single technology can be applied for authentication of all commodities. Nevertheless, many useful applications have been developed for many food commodities. However, the use of these applications in practice is still in its infancy. This is largely because for each single application, new spectral databases need to be built and maintained. Therefore, apart from developing applications, a focus on sharing and re-use of data and calibration transfers is pivotal to remove this bottleneck and to increase the implementation of these technologies in practice.

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Nondestructive detection of maturity of watermelon by spectral characteristic using NIR diffuse transmittance technique

Cited by 30 publications

References 31 publications

Non-Destructive Identification and Estimation of Granulation in Honey Pomelo Using Visible and Near-Infrared Transmittance Spectroscopy Combined with Machine Vision Technology

Non-Destructive Identification and Estimation of Granulation in Honey Pomelo Using Visible and Near-Infrared Transmittance Spectroscopy Combined with Machine Vision Technology

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

Handheld Devices for Food Authentication and Their Applications: A Review

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