Wavelet Analysis of Signals in Agriculture and Food Quality Inspection

Singh, Chandra B.; Choudhary, Ruplal; Jayas, Digvir S.; Paliwal, Jitendra

doi:10.1007/s11947-008-0093-7

Cited by 59 publications

(28 citation statements)

References 54 publications

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“…WT is a popular and powerful technique used in chemometrics and signal processing (Li et al 2000;Sorensen and Jepsen 1998;Drai et al 2002;Singh et al 2008), and it has many advantages in the extraction of relevant information from spectral data. In this study, WT was proposed as a preprocessing method for removing spectral noise, reducing spectral dimensionality, and extracting new eigenvectors.…”

Section: Spectral Acquisition and Preprocessingmentioning

confidence: 99%

Detection of Organic Acids and pH of Fruit Vinegars Using Near-Infrared Spectroscopy and Multivariate Calibration

Liu

Wang

et al. 2009

Food Bioprocess Technol

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Near-infrared (NIR) spectroscopy was investigated to determine the acetic, tartaric, formic acids and pH of fruit vinegars. Optimal partial least squares (PLS) models were developed with different preprocessing. Simultaneously, the performance of least squares-support vector machine (LS-SVM) models was compared with three kinds of inputs, including wavelet transform (WT), latent variables, and effective wavelengths (EWs). The results indicated that all LS-SVM models outperformed PLS models. The optimal correlation coefficient (r), root mean square error of prediction and bias for validation set were 0.9997, 0.3534, and −0.0110 for acetic acid by WT-LS-SVM; 0.9985, 0.1906, and 0.0025 for tartaric acid by WT-LS-SVM; 0.9987, 0.1734, and 0.0012 for formic acid by EW-LS-SVM; and 0.9996, 0.0842, and 0.0012 for pH by WT-LS-SVM, respectively. The results indicated that NIR spectroscopy (7,800-4,000 cm −1 ) combined with LS-SVM could be utilized as a precision method for the determination of organic acids and pH of fruit vinegars.

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Section: Spectral Acquisition and Preprocessingmentioning

confidence: 99%

Detection of Organic Acids and pH of Fruit Vinegars Using Near-Infrared Spectroscopy and Multivariate Calibration

Liu

Wang

et al. 2009

Food Bioprocess Technol

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“…Such an approach has been widely used in fruit/crop grading, classification and removal before shipment [3][4][5][6]. Computational and statistical methodologies have been provided [7][8][9][10][11][12][13][14][15][16]. In the case of producing seed yams, the problem is much simpler than the general problem mentioned above for fruits and crops; we can assume a regular pattern of yams (see Figure 1) and do not have to strictly check yam damage, because the purpose here is to know the shape of yams quickly without the use of many devices (i.e., a low-cost way).…”

Section: Introductionmentioning

confidence: 99%

Bayesian Methods for Predicting the Shape of Chinese Yam in Terms of Key Diameters

Kayano

Kyo

Hachiya

2017

Advances in Agriculture

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This paper proposes Bayesian methods for the shape estimation of Chinese yam (Dioscorea opposita) using a few key diameters of yam. Shape prediction of yam is applicable to determining optimal cutoff positions of a yam for producing seed yams. Our Bayesian method, which is a combination of Bayesian estimation model and predictive model, enables automatic, rapid, and lowcost processing of yam. After the construction of the proposed models using a sample data set in Japan, the models provide whole shape prediction of yam based on only a few key diameters. The Bayesian method performed well on the shape prediction in terms of minimizing the mean squared error between measured shape and the prediction. In particular, a multiple regression method with key diameters at two fixed positions attained the highest performance for shape prediction. We have developed automatic, rapid, and low-cost yam-processing machines based on the Bayesian estimation model and predictive model. Development of such shape prediction approaches, including our Bayesian method, can be a valuable aid in reducing the cost and time in food processing.

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“…Wavelets have been already used for measuring the quality of fruits (Singh et al 2010). The wavelet algorithm decomposes the original signal coming from the receiving device into a number of details and approximations in the frequency and time domains.…”

Section: Introductionmentioning

confidence: 99%

A Noninvasive Method to Identify Larvae in Apples Based on Wavelets

Godínez‐García

Celis-Porras

Guerrero-Rivera

et al. 2010

Food Bioprocess Technol

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Foodstuffs often experience great damage as a result of various forms of infestation or plague. A very common plague is a form of larval infestation that attacks several fruits, including apples. A novel method to identify larval infestations in apples is introduced. This noninvasive method is based on the use of ultrasonic waves, wavelets, and statistical analysis. A wavelet algorithm was preferred over other techniques because it allows a better description of the ultrasonic signal received from the fruit. A total of 998 red delicious apples were sampled, of which, 291 apples were infested by Cydia pomonella. All samples were scanned using ultrasonic waves, and reflecting signals were analyzed with wavelets to determine whether the fruit was intact or infested by larva. The experiments were validated using normality tests and analysis of variance and corroborated by comparing the results with the physical evidence in the specimens. The results show that the signal is attenuated at the center of the apple, which means that the density inside the apple changes as a result of the presence of larva. In addition, the proposed method effectively detected (p<0.01) the presence of larva within the fruit in real time.

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Wavelet Analysis of Signals in Agriculture and Food Quality Inspection

Cited by 59 publications

References 54 publications

Detection of Organic Acids and pH of Fruit Vinegars Using Near-Infrared Spectroscopy and Multivariate Calibration

Detection of Organic Acids and pH of Fruit Vinegars Using Near-Infrared Spectroscopy and Multivariate Calibration

Bayesian Methods for Predicting the Shape of Chinese Yam in Terms of Key Diameters

A Noninvasive Method to Identify Larvae in Apples Based on Wavelets

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