Discrimination between Glutinous and Non-Glutinous Rice by Vibrational Spectroscopy. II: Effects of Spectral Pre-Treatment on the Classification of the Two Types of Rice

1998 ◽  
Vol 6 (1) ◽  
pp. 251-258 ◽  
Author(s):  
Yoshisato Ootake ◽  
Serge Kokot
Keyword(s):  
Vibrational Spectroscopy ◽  
Amylose Content ◽  
Moving Average ◽  
Scatter Correction ◽  
Glutinous Rice ◽  
Rice Samples ◽  
Pre Treatment ◽  
Spectral Sampling ◽  
Ft Raman

To measure the amylose content of rice, discrimination between glutinous and non-glutinous rice by vibrational spectroscopy was performed. It was previously demonstrated that classification of raw spectra by the SIMCA method showed the FT-Raman technique provided the best discrimination of classes. In this paper, the effects of spectral pre-treatments such as differentiation and multiplicative scatter correction (MSC) on classification of the rice samples are presented. For FT-NIR DRIFT measurements, differences in classification following different pre-treatments were relatively small but the best classification was obtained with the 2nd derivative pre-treatment, although precision was generally poor. With the PAS spectral sampling method, the classification was better after MSC pre-treatment of raw spectra than either after conversion to the 1st derivative or 2nd derivative. For FT-Raman, the best result was obtained with the MSC pre-treatment. The different effects of pre-treatment on the classification of spectra of the two types of rice probably reflect the information contained in the width of the absorbance bands, because the spectra were collected at 4 cm−1 step with 8 cm−1 resolution. Since the differentiation procedure follows the moving average, very small bands would be eliminated in the process, and would not contribute to the classification analysis. It was noted that effects of fluorescence in the FT-Raman spectra were not removed, even after the MSC pre-treatment.

Discrimination between Glutinous and Non-Glutinous Rice by Vibrational Spectroscopy. I: Comparison of FT-NIR DRIFT, PAS and Raman Spectroscopy

1998 ◽  
Vol 6 (1) ◽  
pp. 241-249 ◽  
Author(s):  
Yoshisato Ootake ◽  
Serge Kokot
Keyword(s):  
Raman Spectroscopy ◽  
Water Content ◽  
Near Infrared ◽  
Amylose Content ◽  
Spectroscopic Techniques ◽  
Glutinous Rice ◽  
Ft Raman Spectroscopy ◽  
Diffuse Reflectance Infrared ◽  
Ft Raman

Rice is a major cereal crop in Japan and in Asia. Its taste is determined by such factors as protein and water content as well as stickiness. It has been well established that protein and water content can be estimated by near infrared spectroscopic measurements. However, the measurement of amylose content, which is closely related to rice stickiness, at present, must be carried out by wet chemical methods. Vibrational spectroscopic techniques are possible alternative approaches for the determination of amylose content in rice and, in this paper, we report on the initial steps for the development of methodology for this purpose, namely on the comparison of FT-NIR DRIFT (Diffuse Reflectance Infrared Fourier Transform Spectroscopy), PAS (Photo-Acoustic spectroscopy) and FT-Raman spectroscopy for the discrimination of glutinous and non-glutinous rice. Perkin-Elmer System 2000 FTIR (equipped with DRIFT and PAS accessories) and System 2000 NIR FT-Raman spectrometers were used to collect spectra from ground samples of seven glutinous and 12 non-glutinous rice. When SIMCA (Soft Independent Modelling of Class Analogy) was used to classify raw spectral data, the best discrimination was achieved with the FT-Raman results followed by those from the PAS measurements. FT-Raman spectra of some samples of non-glutinous rice showed strong fluorescence effects. When these samples were excluded from analysis, modelling and classification improved.

scholarly journals Feasibility of Using VIS/NIR Spectroscopy and Multivariate Analysis for Pesticide Residue Detection in Tomatoes

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 196
Author(s):  
Araz Soltani Nazarloo ◽  
Vali Rasooli Sharabiani ◽  
Yousef Abbaspour Gilandeh ◽  
Ebrahim Taghinezhad ◽  
Mariusz Szymanek ◽  
...  
Keyword(s):  
Near Infrared ◽  
Cross Validation ◽  
Low Cost ◽  
Moving Average ◽  
Nir Spectroscopy ◽  
Control Treatment ◽  
Residue Detection ◽  
Non Destructive ◽  
Reflective Spectroscopy

The purpose of this work was to investigate the detection of the pesticide residual (profenofos) in tomatoes by using visible/near-infrared spectroscopy. Therefore, the experiments were performed on 180 tomato samples with different percentages of profenofos pesticide (higher and lower values than the maximum residual limit (MRL)) as compared to the control (no pesticide). VIS/near infrared (NIR) spectral data from pesticide solution and non-pesticide tomato samples (used as control treatment) impregnated with different concentrations of pesticide in the range of 400 to 1050 nm were recorded by a spectrometer. For classification of tomatoes with pesticide content at lower and higher levels of MRL as healthy and unhealthy samples, we used different spectral pre-processing methods with partial least squares discriminant analysis (PLS-DA) models. The Smoothing Moving Average pre-processing method with the standard error of cross validation (SECV) = 4.2767 was selected as the best model for this study. In addition, in the calibration and prediction sets, the percentages of total correctly classified samples were 90 and 91.66%, respectively. Therefore, it can be concluded that reflective spectroscopy (VIS/NIR) can be used as a non-destructive, low-cost, and rapid technique to control the health of tomatoes impregnated with profenofos pesticide.

scholarly journals The Use of UV Spectroscopy and SIMCA for the Authentication of Indonesian Honeys According to Botanical, Entomological and Geographical Origins

Molecules ◽  
2021 ◽  
Vol 26 (4) ◽  
pp. 915
Author(s):  
Diding Suhandy ◽  
Meinilwita Yulia
Keyword(s):  
Spectral Data ◽  
Uv Spectroscopy ◽  
Moving Average ◽  
Food Product ◽  
Classification Method ◽  
Economic Losses ◽  
Food Fraud ◽  
Authentication System ◽  
Uv Visible

As a functional food, honey is a food product that is exposed to the risk of food fraud. To mitigate this, the establishment of an authentication system for honey is very important in order to protect both producers and consumers from possible economic losses. This research presents a simple analytical method for the authentication and classification of Indonesian honeys according to their botanical, entomological, and geographical origins using ultraviolet (UV) spectroscopy and SIMCA (soft independent modeling of class analogy). The spectral data of a total of 1040 samples, representing six types of Indonesian honey of different botanical, entomological, and geographical origins, were acquired using a benchtop UV-visible spectrometer (190–400 nm). Three different pre-processing algorithms were simultaneously evaluated; namely an 11-point moving average smoothing, mean normalization, and Savitzky–Golay first derivative with 11 points and second-order polynomial fitting (ordo 2), in order to improve the original spectral data. Chemometrics methods, including exploratory analysis of PCA and SIMCA classification method, was used to classify the honey samples. A clear separation of the six different Indonesian honeys, based on botanical, entomological, and geographical origins, was obtained using PCA calculated from pre-processed spectra from 250–400 nm. The SIMCA classification method provided satisfactory results in classifying honey samples according to their botanical, entomological, and geographical origins and achieved 100% accuracy, sensitivity, and specificity. Several wavelengths were identified (266, 270, 280, 290, 300, 335, and 360 nm) as the most sensitive for discriminating between the different Indonesian honey samples.

NIR-FT/Raman Spectroscopy for Nutritional Classification of Cereal Foods

2005 ◽  
Vol 82 (6) ◽  
pp. 660-665 ◽  
Author(s):  
Miryeong Sohn ◽  
David S. Himmelsbach ◽  
Sandra E. Kays ◽  
Douglas D. Archibald ◽  
Franklin E. Barton
Keyword(s):  
Raman Spectroscopy ◽  
Ft Raman Spectroscopy ◽  
Ft Raman

NIR-FT/Raman Spectroscopy for Nutritional Classification of Cereal Foods

NIR news ◽  
2006 ◽  
Vol 17 (5) ◽  
pp. 6-7 ◽  
Author(s):  
Miryeong Sohn ◽  
David S. Himmelsbach ◽  
Sandra E. Kays ◽  
Douglas D. Archibald ◽  
Franklin E. Barton
Keyword(s):  
Raman Spectroscopy ◽  
Ft Raman Spectroscopy ◽  
Ft Raman

Classification of Biological Spectrum Based on Principal Component Cluster Analysis

2012 ◽  
Vol 605-607 ◽  
pp. 2245-2248
Author(s):  
Lian Shun Zhang ◽  
Ai Juan Shi
Keyword(s):  
Cluster Analysis ◽  
Biological Tissue ◽  
Fiber Optic ◽  
Scatter Correction ◽  
Principal Component ◽  
Correction Method ◽  
Tissue Phantoms ◽  
Spectrum Feature ◽  
Biological Spectrum

Spectrums of 17 biological tissue phantoms were measured using the fiber-optic spectrometer. Then, the spectrum was preprocessed by multiplicative scatter correction method to devoice the spectrum. Afterwards the features of the spectrum were extracted via principal component analysis. Ultimately, we applied cluster analysis for the spectral features. The results showed that the accumulated credibility of the first 12 spectral principal components was 99.86% for the spectrum after preprocessing; indicating that this spectrum feature extraction might be done in the case of losing no key information. And the results showed that the 17 biological tissue phantoms can be divided into four main categories according their optical features.

scholarly journals Effect of moisturizing pre-treatment of dietary fibre preparations on formation of gluten network during model dough mixing – A study with application of FT-IR and FT-Raman spectroscopy

LWT ◽  
2020 ◽  
Vol 121 ◽  
pp. 108959 ◽  
Author(s):  
Agnieszka Nawrocka ◽  
Magdalena Krekora ◽  
Zbigniew Niewiadomski ◽  
Monika Szymańska-Chargot ◽  
Ada Krawęcka ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Dietary Fibre ◽  
Dough Mixing ◽  
Ft Raman Spectroscopy ◽  
Ft Ir ◽  
Pre Treatment ◽  
Ft Raman

Pre-treatment and elimination systems of toxic industrial waste and sludges. The case study of the department of Attika

1997 ◽  
Vol 36 (2-3) ◽  
pp. 91-100
Author(s):  
C. Malliaros ◽  
A. Guitonas
Keyword(s):  
Liquid Waste ◽  
Public Works ◽  
Toxic Waste ◽  
Alternative Methods ◽  
Toxic Substances ◽  
Pre Treatment ◽  
Evaluation Of Data ◽  
Methods Of Treatment

In the departments of Attika and Viotia there are about 1,100 industries. The total amount of produced waste from the above mentioned branches is 20 × 106 t/year, 600,000t of which contain toxic substances. By contract from October 1st, 1992 between the Ministry of the Environment, Regional Planning and Public Works and three collaborating offices (among them the office of C. Malliaros) has been assigned to the latter, the realisation of a study of the management of toxic waste (liquid) and sludges, in the Departments of Attika and Viotia. The study presented in this paper investigates the following: - collection and evaluation of data and estimation of the hydraulic and pollution loads - classification of industries according to sewage facilities - further possibilities for changes of improvements in the production process of the industrial branches for the reduction of pollution and representation of these industries on maps - proposals for the collection and transport of the liquid toxic waste and sludges - pre-treatment at the source and disposal of the liquid waste and sludges - presentation of administrative and legislative regulations - forming a policy for the management and monitoring of this waste - technical and financial evaluation and investigation of the alternative methods of treatment - suggestions for the development of the area concerning the activities and the expenses at various levels.

scholarly journals APPLICATION OF AUTO REGRESSIVE MODELS OF WAVELET SUB-BANDS FOR CLASSIFYING TERAHERTZ PULSE MEASUREMENTS

2007 ◽  
Vol 15 (04) ◽  
pp. 551-571 ◽  
Author(s):  
XIAOXIA YIN ◽  
BRIAN W.-H. NG ◽  
DEREK ABBOTT ◽  
BRADLEY FERGUSON ◽  
SILLAS HADJILOUCAS
Keyword(s):  
Wavelet Transforms ◽  
Moving Average ◽  
Cell Types ◽  
Classification Systems ◽  
Arma Models ◽  
Security Applications ◽  
Powder Samples ◽  
Auto Regressive ◽  
Normal Human

This paper presents an approach for automatic classification of pulsed Terahertz (THz), or T-ray, signals highlighting their potential in biomedical, pharmaceutical and security applications. T-ray classification systems supply a wealth of information about test samples and make possible the discrimination of heterogeneous layers within an object. In this paper, a novel technique involving the use of Auto Regressive (AR) and Auto Regressive Moving Average (ARMA) models on the wavelet transforms of measured T-ray pulse data is presented. Two example applications are examined — the classification of normal human bone (NHB) osteoblasts against human osteosarcoma (HOS) cells and the identification of six different powder samples. A variety of model types and orders are used to generate descriptive features for subsequent classification. Wavelet-based de-noising with soft threshold shrinkage is applied to the measured T-ray signals prior to modeling. For classification, a simple Mahalanobis distance classifier is used. After feature extraction, classification accuracy for cancerous and normal cell types is 93%, whereas for powders, it is 98%.

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