KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Settings

Subject: search.filters.subject.Near infrared

Search Results

Now showing 1 - 4 of 4
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    A COMPREHENSIVE OVERVIEW OF NEAR INFRARED AND INFRARED SPECTROSCOPY FOR DETECTING THE ADULTERATION ON FOOD AND AGRO-PRODUCTS—A CRITICAL ASSESSMENT
    (2022-01-01)
    Sitorus, Agustami
    ;
    Lapcharoensuk, Ravipat
    In the past decade, fast and non-destructive methods based on spectroscopy technology have been studied to detect and discriminate against food adulteration and agro-products. Numerous linear and nonlinear chemometric approaches have been developed for spectroscopy analysis. Recently, various approaches have been developed for spectroscopic calibration modelling to detect and discriminate adulteration food and agro-products. This article discusses the application of spectroscopy technology, including near infrared and infrared, in detecting and discriminating the adulteration of food and agro-products based on recent research and delivered a critical assessment on this topic to serve as lessons from current studies and future outlooks. The current state-of-the-art techniques, including detection and classification of various adulteration in food and agro-products, have been addressed in this paper. Key findings from this study, near infrared and infrared spectroscopy is a non-destructive, rapid, simple-preparation, analytical rapidity, and straightforward method for classification and determination of adulteration in the food and agro-products so it is suitable for large-scale screening and on-site detection. Although there are still some unsatisfactory research results, especially in detecting tiny adductors, these technologies can potentially detect any adulteration in the various food and agro-products at an economically viable level, at least for the initial screening process. In that respect, near infrared and infrared spectroscopy should be expanded to cover all food and agro-products sold in the market. Only then will there be an acceptable deterrent in place to stop adulteration activity in widely consumed food and agro-products ingredients
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Evaluation of salt content of curry soup containing coconut milk by near infrared spectroscopy
    (2018-06-01)
    Cheevitsopon, Ekkapong
    ;
    Sirisomboon, Panmanas
    A feasibility study was performed to assess whether near infrared spectroscopy could evaluate the salt content of curry soup containing coconut milk. The soup samples were from the mixing tank, a water content adjusted tank, the ultra-high temperature pipe, and laminated containers of a food processor plant. In addition, fish sauce adjusted samples made from the same recipe but with increasing or decreasing (±30%, 60%, and 90%) sauce content were prepared. There were 113 samples in total, which were scanned using a Fourier-transform near infrared spectrometer. The prediction models for salt content were established using near infrared spectral data in conjunction with partial least squares regression. Calibration models developed using all of the samples were validated using leave-one-out cross validation and test set validation. The unadjusted sample models were validated using test set validation. The results showed that both validation methods for the calibration models using all of the samples provided similar model performance where the r<sup>2</sup>, root mean square error of calibration/root mean square error of prediction, and residual predictive deviation were 0.956, 0.065%, and 4.77 for cross validation and 0.954, 0.064%, and 4.64 for the test set, respectively. However, the salt unadjusted sample model showed better performance where the r<sup>2</sup>, RMSEP, and RPD were respectively 0.963, 0.043%, and 5.23, indicating that excellent models can be developed to determine the salt content of curry soup containing coconut milk for any applications, including quality assurance.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Classification of sweet corn based on storage time after harvest using near infrared spectroscopy
    (2017-03-21)
    Suktanarak, S.
    ;
    Supprung, P.
    ;
    Teerachaichayut, S.
    The freshness of sweet corns is important for production of canned sweet corn. The quality of sweet corns changes rapidly after harvest. Sweet corns should be processed through a production line as fast as possible after harvest. Therefore, some methods of classification of sweet corns based on storage time after harvest are needed. In this study, near infrared (NIR) spectroscopy operating in reflectance mode (1000-2500 nm) and interactance mode (588-1091) were investigated as methods of classification. Sweet corns both with and without husk were tested. Samples (n=120) were scanned with a NIR spectrophotometer every 6 h after harvest. They were then classified into two groups (0 and 1) with a 24-h after harvest cut-off time between the two groups (<24 h and ≥24 h). Classification models were established and validated with a calibration set (n=80), and then the accuracies of the models were evaluated with a prediction set (n=40), using a partial least squares discriminant analysis (PLSDA). It was found that second derivative spectral pretreatment gave the best results for NIR operating in reflectance mode. Regarding prediction accuracy, showed the best accuracies for both unhusked and husked sweet corns (100%), while it was found that mean center and second derivative spectral pretreatment gave good results for NIR operating in interactance mode. The predictive accuracies for unhusked and husked sweet corns obtained 90 and 97.5%, respectively. All of the results demonstrated that NIR spectroscopy has a real potential for non-destructive classification of sweet corns based on storage time after harvest.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    An effective classification procedure for diagnosis of prostate cancer in near infrared spectra
    (2010-05-01)
    Kim, Seoung Bum
    ;
    Temiyasathit, Chivalai
    ;
    Bensalah, Karim
    ;
    Tuncel, Altug
    ;
    Cadeddu, Jeffrey
    The main purpose of this study is to develop an effective classification procedure that discriminates between normal spectra and cancerous spectra in near infrared (NIR) spectroscopic data in which the classes are highly imbalanced and overlapped. Our proposed procedure consists of several steps. First, to ensure the comparability between spectra, normalization was done by dividing each spectral point by the area of the total intensity of the spectrum. Second, clustering analysis was performed with these normalized spectra to separate the spectra that represent the normal pattern from a mixed group that contains both normal and tumor spectra. Third, we conducted two-stage classification, the first being an effort to construct a classification model with the labels obtained from the preceding clustering analysis and the second being a classification to focus on the mixed group classified from the first classification model. To increase the accuracy, the second classification model was constructed based on the selected features that capture important characteristics of the spectral data. Our proposed procedure was evaluated by its classification ability in testing samples using a leave-one-out cross validation technique, yielding acceptable classification accuracy. © 2009 Elsevier Ltd. All rights reserved.