Research advances in nondestructive determination of internal quality in watermelon/melon: A review

Sun, Tong; Huang, Kuan-Chun; Xu, Huirong; Ying, Yibin

doi:10.1016/j.jfoodeng.2010.05.019

Cited by 85 publications

(24 citation statements)

References 66 publications

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“…2 However, consumer acceptability for melon is determined by not only sugar concentration at harvest, but also its texture and maturity. 3 Tissuē rmness of muskmelon fruit can decrease by 400% between 30 and 50 days post-anthesis measured by Lester, which was thought to a®ect both duration of shelf-life and intensity of sweetness. 4 Most instrumental techniques measuring these properties are destructive and involve a considerable amount of manual work.…”

Section: Introductionmentioning

confidence: 96%

Nondestructive determination of soluble solids and firmness in mix-cultivar melon using near-infrared CCD spectroscopy

Lü

Liu

et al. 2015

J. Innov. Opt. Health Sci.

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Nondestructive evaluation of melon quality is in great need of comprehensive study. Soluble solids content (SSC) and¯rmness are the two indicators of melon internal quality that mostly a®ect consumer acceptance. To provide guidance for fruit classi¯cation, internal quality standards was preliminarily established through sensory test, as: Melon with SSC over 12 Brix, rmness 4-5.5 kgf Á cm À2 were considered as satisfactory class sample; and SSC over 10 Brix, rmness 3.5-6.5 kgf Á cm À2 as average class sample. The near infrared (NIR) nondestructive detection program was set as spectra collected from the stylar-end, Brix expressed by the average SSC of inner and outer mesocarp, each cultivar of melon was detected with its own optimum integration time, and the second derivative algorithm was used to equalize them. Using wavelength selected by genetic algorithms (GA), a robust SSC model of mix-cultivar melon was established, the root mean standard error of cross-validation (RMSECV) was 0.99 and the ratio performance deviation (RPD) nearly reached 3.0, which almost could meet the accuracy requirement of 1.5 Brix. Firmness model of mix-cultivar melon was acceptable but inferior.

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Section: Introductionmentioning

confidence: 96%

Nondestructive determination of soluble solids and firmness in mix-cultivar melon using near-infrared CCD spectroscopy

Lü

Liu

et al. 2015

J. Innov. Opt. Health Sci.

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“…These tests have been increasingly used all fields of animal production and agriculture. For example, detecting the ripening or quality of fruits is among the applications of non-destructive testing (Chen & Sun, 1991;Sun et al, 2010). When nondestructive tests are used in live organisms, it will be possible to obtain information from those organisms which otherwise could only be extracted by destructive tests under normal circumstances.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Procedure for the Detection and Classification of Chickens Infected by Clostridium Perfringens Based on their Vocalization

Sadeghi

Banakar

Khazaee

et al. 2015

Rev. Bras. Cienc. Avic.

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In this study, an intelligent method was implemented for the detection and classification of chickens by infected Clostridium perfringens type A based on their vocalization. To this aim, the birds were first divided into two groups that were placed in separate cages with 15 chickens each. Chickens were inoculated with Clostridium perfringens type A on day 14. In order to ensure the absence of secondary diseases and their probable effect on bird vocalization, vaccines for common diseases were administered. During 30 days of the experiment, chicken vocalization was recorded every morning at 8 a.m. using a microphone and a data collection card under equal and controlled conditions. Sound signals were investigated in time domains, and 23 features were selected. Using Fisher Discriminate Analysis (FDA), five of the most important and effective features were chosen. Neural Network Pattern Recognition (NNPR) structure with one hidden layer was applied to detect signals and classifying healthy and unhealthy chickens. Firstly, this neural network was trained with 34 samples, after which eight samples were tested for accuracy. Classification accuracy was 66.6 and 100% for days 16 and 22; i.e., two and eight days after the disease, respectively. The results of this study demonstrated the usefulness and effectiveness of intelligent methods for diagnosing diseases in chickens.

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“…5 In order to realize quality grading rapidly for watermelon nondestructively, the variables were selected by Monte-Carlo uninformative variable eliminate (MC-UVE) in building the partial least squares (PLS) regression model of SSC. 6 Despite the widespread application, pre-processed spectral data is often not well understood.…”

Section: Introductionmentioning

confidence: 99%

Establishment of a comprehensive indicator to nondestructively analyze watermelon quality at different ripening stages

Song²,

ShengNan

et al. 2014

J. Innov. Opt. Health Sci.

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Two nondestructive methods based on visible and near-infrared (VIS-NIR) spectroscopy and X-ray image have been used for the evaluation of watermelon quality. The prediction performance based on partial least squares (PLS) by di®use transmittance measurement (500-1010 nm) was evaluated for chemical quality attributes SSC (Rc ¼ 0:903; RMSEC ¼ 0:572% Brix; Rp ¼ 0:862; RMSEP ¼ 0:717% Brix; RPD ¼ 1:83), lycopene (Rc ¼ 0:845; RMSEC ¼ 0:266 mg/ 100 gFW; Rp ¼ 0:751; RMSEP ¼ 0:439 mg/100 gFW; RPD ¼ 1:13) and moisture (Rc ¼ 0:917; RMSEC ¼ 0:280%; Rp ¼ 0:937; RMSEP ¼ 0:276%; RPD ¼ 2:79). The X-ray calibration linear equations developed by extracting the appropriate gray threshold were su±ciently precise for volume (R 2 ¼ 0:986) and weight (R 2 ¼ 0:993). In order to optimize prediction model of watermelon quality in growth period, multivariate multi-block technique factor analysis enabled integration of these traits: chemical information is related to physical information. Applying principle component analysis to extract common factors and varimax with Kaiser normalization to improve explanatory, the comprehensive indicator based on variances was established satisfactorily with Rc ¼ 0:94, RMSEC ¼ 0:244, Rp ¼ 0:93, RMSEP ¼ 0:344 and RPD ¼ 2:00. A comparison of these models indicates that the comprehensive indicator determined only by portable VIS-NIR spectrometer appears as a suitable method for appraising watermelon quality nondestructively on the plant at di®erent ripen stages. This method contributes to infer the This is an Open Access article published by World Scienti¯c Publishing Company. It is distributed under the terms of the Creative Commons Attribution 3.0 (CC-BY) License. Further distribution of this work is permitted, provided the original work is properly cited.

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Research advances in nondestructive determination of internal quality in watermelon/melon: A review

Cited by 85 publications

References 66 publications

Nondestructive determination of soluble solids and firmness in mix-cultivar melon using near-infrared CCD spectroscopy

Nondestructive determination of soluble solids and firmness in mix-cultivar melon using near-infrared CCD spectroscopy

An Intelligent Procedure for the Detection and Classification of Chickens Infected by Clostridium Perfringens Based on their Vocalization

Establishment of a comprehensive indicator to nondestructively analyze watermelon quality at different ripening stages

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