Near infrared spectroscopy and aquaphotomics evaluation of the efficiency of solar dehydration processes in pineapple slices

2021 ◽  
pp. 096703352110543
Author(s):  
Tiziana MP Cattaneo ◽  
Maurizio Cutini ◽  
Alessandro Cammerata ◽  
Annamaria Stellari ◽  
Laura Marinoni ◽  
...  
Keyword(s):  
Near Infrared ◽  
Wavelength Region ◽  
Statistical Processing ◽  
Principal Component ◽  
Titratable Acidity ◽  
Ventilation Rate ◽  
Spectra Analysis ◽  
Linear Discriminant ◽  
Water Matrix ◽  
Dehydration Processes

Parallel transformation tests on pineapple slices using two micro drying plants (M1 and M2) operating with solar energy were carried out. Method M1 consisted of an active fan at the top, whose ventilation rate depended on the internal temperature. Method M2 had a continuously working fan at the bottom. The dehydration performance of these two micro-plants was compared by collecting spectra from pineapple slices in reflectance mode (900–1600 nm) at three different times: (0) process start, (1) during the process [48 h] and (2) process end [56 h]. Simultaneously, dry matter, titratable acidity (SH°), pH and aw (water activity) were measured. For these parameters, significant differences ( p < 0.05) were detected between the fresh (t = 0) and the dried product (t = 56). Near infrared (NIR) spectra analysis was carried out according to previously published methods. Spectral data in the wavelength region from 1300 to 1550 nm underwent statistical processing to perform aquaphotomics evaluation and chemometrics methods such as PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis). The Aquagrams highlighted differences among fresh, half-dried and dried slices where water molecules were highly organized between the water matrix coordinates C1 to C3 at t = 0 and C2 to C6 for the other evaluated times. The PCA could explain about 98% of the total variance in the PC1–PC3 scores plot. And the additional LDA classified the NIR spectra with an accuracy of 100, 98 and 83% for t = 0, t = 56-M1 and t = 56-M2, respectively. Such preliminary results suggest the applicability of Aquaphotomics and chemometrics for the continuous monitoring of fruit drying processes using an adequate NIR probe. Further experiments are already in progress.

Detection of Aflatoxin B1 in Pistachio Kernels Using Visible/Near-Infrared Hyperspectral Imaging

2019 ◽  
Vol 62 (5) ◽  
pp. 1065-1074
Author(s):  
Qifang Wu ◽  
Huirong Xu
Keyword(s):  
Hyperspectral Imaging ◽  
Near Infrared ◽  
Correlation Coefficients ◽  
Principal Component ◽  
Nutrient Content ◽  
Aflatoxin Contamination ◽  
Quantitative Prediction ◽  
Spectra Analysis ◽  
Linear Discriminant ◽  
Standard Normal Variate

Abstract. Pistachios are susceptible to aflatoxin contamination because of their rich nutrient content. Hyperspectral imaging (HSI), a new method for collecting spectral and image information, has been successfully employed in contamination research to classify staple agricultural products, such as maize, that are contaminated with aflatoxins. However, only a few studies have been conducted on the nondestructive discrimination among contaminated nuts using HSI for both qualitative and quantitative purposes. Thus, the feasibility of directly detecting aflatoxin B1 (AFB1) in individual pistachio kernels using visible/near-infrared HSI (VNIR HSI) was explored in this study. A total of 300 pistachio kernels were randomly selected to prepare target samples that were artificially contaminated with 5, 10, 20, 30, or 50 ppb (parts per billion) of AFB1. Principal component analysis (PCA) showed an overall separation trend between the control and all contaminated kernels. Accuracies greater than 90.0% were obtained by linear discriminant analysis (LDA) for samples that were artificially contaminated with different concentrations of AFB1 based on spectra at 694 to 988 nm that had been preprocessed with standard normal variate (SNV) and Savitzky-Golay (SG) smoothing. The correlation coefficients of calibration and validation (rc and rv) from stepwise multiple linear regression (SMLR) models were all &gt;0.9100. Moreover, five key wavelengths (708, 771, 892, 915, and 941 nm) closely associated with AFB1 contamination were identified using principal component spectra analysis. Generally, the results indicated that VNIR HSI could be employed for preliminary screening of pistachio kernels that were artificially contaminated with AFB1, even at the 5 ppb level. However, the quantitative prediction of the specific AFB1 concentration needed to be further improved. Keywords: Aflatoxins, Detection analysis, Hyperspectral information, Pistachios, Visible/near-infrared.

scholarly journals Monitoring of water spectral patterns of lactobacilli development as a tool for rapid selection of probiotic candidates

2017 ◽  
Vol 25 (6) ◽  
pp. 423-431 ◽  
Author(s):  
Aleksandar Slavchev ◽  
Zoltan Kovacs ◽  
Haruki Koshiba ◽  
Gyorgy Bazar ◽  
Bernhard Pollner ◽  
...  
Keyword(s):  
Spectral Data ◽  
Near Infrared ◽  
Wavelength Region ◽  
Principal Component ◽  
Lactobacillus Bulgaricus ◽  
Probiotic Bacteria ◽  
Least Squares Regression ◽  
Linear Discriminant ◽  
Spectral Patterns

Nowadays a quick and inexpensive method, which allows rapid, in vivo comprehensive probiotic bacteria identification, is needed. To elucidate a new concept to evaluate probiotic bacteria, near infrared spectroscopy with aquaphotomics were applied to monitor the growth of eight Lactobacillus bulgaricus and one Lactobacillus gasseri bacteria strains. Their resistance to low pH (1.8) in the presence of pepsin and bile were measured and further used as reference data for analysis of the simultaneously acquired spectral data. The acquired spectral data in the region of 1100–1300 nm were subjected to various methods for multivariate data analyses—principal component analysis, linear discriminant analysis, soft independent modeling of class analogy, and partial least squares regression. The results showed high accuracy of bacteria strains classification according to their resistance and the potential of the tested wavelength region for rapid selection and prediction of some basic phenotypic characteristics of probiotic candidates. Results of the current study also revealed different suitability of each growth stage when using near infrared spectra for the classification of bacteria strains.

scholarly journals Potential of fourier transformed near-infrared (FT-NIR) spectroscopy for rapid analysis of elderberry (Sambucus nigra L.) fruits

2019 ◽  
Vol 37 (No. 1) ◽  
pp. 21-28 ◽  
Author(s):  
Virág Csorba ◽  
Marietta Fodor ◽  
Szilvia Kovács ◽  
Magdolna Tóth
Keyword(s):  
Near Infrared ◽  
Reference Data ◽  
Scatter Correction ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Titratable Acidity ◽  
Soluble Solids ◽  
Breeding Method ◽  
Linear Discriminant ◽  
Fruit Samples

Fruit samples were analysed to investigate the suitability of Fourier transform near infrared spectroscopy (FT-NIR) for the rapid discrimination of elderberry genotypes. Parallel analysis with classical chemical techniques and spectral measurements was performed on 11 cultivars originating from various European countries. The titratable acidity (TA) and soluble solids content (SSC) of the fruit, and the geographical origin and breeding method of the cultivar were used as reference data. Three spectrum transformation methods (standard normal variation, multiplicative scatter correction and first derivative) were applied in the calibration process. The statistical analysis and comparison of the samples was carried out using principal component analysis (PCA) and linear discriminant analysis (LDA). In all cases the analysis demonstrated a correlation between the spectra and both the chemical traits (TA and SSC) of the fruit and the other reference data, indicating that pattern recognition was not a chance occurrence. This work provides the first evidence that the NIR technique can be successfully applied to distinguish between elderberry genotypes on the basis of fruit quality, thus opening up new possibilities in breeding cultivars for food industry purposes.

scholarly journals A fast and low-cost approach to quality control of alcohol-based hand sanitizer using portable near infrared spectrometer and chemometrics

2021 ◽  
pp. 096703352098731
Author(s):  
Adenilton C da Silva ◽  
Lívia PD Ribeiro ◽  
Ruth MB Vidal ◽  
Wladiana O Matos ◽  
Gisele S Lopes
Keyword(s):  
Quality Control ◽  
Discriminant Analysis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Chemical Components ◽  
Linear Discriminant ◽  
Hand Sanitizer

The use of alcohol-based hand sanitizers is recommended as one of several strategies to minimize contamination and spread of the COVID-19 disease. Current reports suggest that the virucidal potential of ethanol occurs at concentrations close to 70%. Traditional methods of verifying the ethanol concentration in such products invite potential errors due to the viscosity of chemical components or may be prohibitively expensive to undertake in large demand. Near infrared (NIR) spectroscopy and chemometrics have already been used for the determination of ethanol in other matrices and present an alternative fast and reliable approach to quality control of alcohol-based hand sanitizers. In this study, a portable NIR spectrometer combined with classification chemometric tools, i.e., partial least square discriminant analysis (PLS–DA) and linear discriminant analysis with successive algorithm projection (SPA–LDA) were used to construct models to identify conforming and non-conforming commercial and laboratory synthesized hand sanitizer samples. Principal component analysis (PCA) was applied in an exploratory data study. Three principal components accounted for 99% of data variance and demonstrate clustering of conforming and non-conforming samples. The PLS–DA and SPA–LDA classification models presented 77 and 100% of accuracy in cross/internal validation respectively and 100% of accuracy in the classification of test samples. A total of 43% commercial samples evaluated using the PLS–DA and SPA–LDA presented ethanol content non-conforming for hand sanitizer gel. These results indicate that use of NIR spectroscopy and chemometrics is a promising strategy, yielding a method that is fast, portable, and reliable for discrimination of alcohol-based hand sanitizers with respect to conforming and non-conforming ethanol concentrations.

scholarly journals Tracing the Geographical Origin of Durum Wheat by FT-NIR Spectroscopy

Foods ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 450 ◽  
Author(s):  
Annalisa De Girolamo ◽  
Marina Cortese ◽  
Salvatore Cervellieri ◽  
Vincenzo Lippolis ◽  
Michelangelo Pascale ◽  
...  
Keyword(s):  
Durum Wheat ◽  
Near Infrared ◽  
Southern Italy ◽  
Geographical Origin ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Classification Model ◽  
North Central ◽  
Linear Discriminant ◽  
The World

Fourier transform near infrared (FT-NIR) spectroscopy, in combination with principal component-linear discriminant analysis (PC-LDA), was used for tracing the geographical origin of durum wheat samples. The classification model PC-LDA was applied to discriminate durum wheat samples originating from Northern, Central, and Southern Italy (n = 181), and to differentiate Italian durum wheat samples from those cultivated in other countries across the world (n = 134). Developed models were validated on a separated set of wheat samples. Different pre-treatments of spectral data and different spectral regions were selected and compared in terms of overall discrimination (OD) rates obtained in validation. The LDA models were able to correctly discriminate durum Italian wheat samples according to their geographical origin (i.e., North, Central, and South) with OD rates of up of 96.7%. Better results were obtained when LDA models were applied to the discrimination of Italian durum wheat samples from those originating from other countries across the world, having OD rates of up to 100%. The excellent results obtained herein clearly indicate the potential of FT-NIR spectroscopy to be used for the discrimination of durum wheat samples according to their geographical origin.

The Authentication of Basmati Rice Using near Infrared Spectroscopy: Some Further Analysis

1995 ◽  
Vol 3 (3) ◽  
pp. 111-117 ◽  
Author(s):  
W.J. Krzanowski
Keyword(s):  
Near Infrared ◽  
Principal Component ◽  
Basmati Rice ◽  
Multivariate Techniques ◽  
Infrared Transmission ◽  
Linear Discriminant ◽  
Canonical Variates ◽  
Rice Samples ◽  
Single Category ◽  
Pooled Samples

The feasibility of using near infrared transmission spectroscopy to discriminate between Basmati and other long-grain rice samples has been demonstrated previously by Osborne et al. In their analysis they pooled samples from different countries of origin into the single category “other” and used the multivariate techniques of principal component analysis and linear discriminant functions to arrive at their conclusions. We reanalyse here their data but without such a major pooling of samples, retaining four groups in the discrimination. Using the multivariate techniques of partial least squares, orthogonal canonical variates and a recently proposed search for “extremeness”, we demonstrate complete support for the previous conclusions.

scholarly journals Evaluation of Visible-Near Infrared Reflectance Spectra of Avocado Leaves as a Non-destructive Sensing Tool for Detection of Laurel Wilt

Plant Disease ◽  
2012 ◽  
Vol 96 (11) ◽  
pp. 1683-1689 ◽  
Author(s):  
Sindhuja Sankaran ◽  
Reza Ehsani ◽  
Sharon A. Inch ◽  
Randy C. Ploetz
Keyword(s):  
Discriminant Analysis ◽  
Vegetation Index ◽  
Near Infrared ◽  
Principal Component ◽  
Persea Americana ◽  
Near Infrared Reflectance ◽  
Laurel Wilt ◽  
Linear Discriminant ◽  
Raffaelea Lauricola ◽  
Non Destructive

Laurel wilt, caused by the fungus Raffaelea lauricola, affects the growth, development, and productivity of avocado, Persea americana. This study evaluated the potential of visible-near infrared spectroscopy for non-destructive sensing of this disease. The symptoms of laurel wilt are visually similar to those caused by freeze damage (leaf necrosis). In this work, we performed classification studies with visible-near infrared spectra of asymptomatic and symptomatic leaves from infected plants, as well as leaves from freeze-damaged and healthy plants, both of which were non-infected. The principal component scores computed from principal component analysis were used as input features in four classifiers: linear discriminant analysis, quadratic discriminant analysis (QDA), Naïve-Bayes classifier, and bagged decision trees (BDT). Among the classifiers, QDA and BDT resulted in classification accuracies of higher than 94% when classifying asymptomatic leaves from infected plants. All of the classifiers were able to discriminate symptomatic-infected leaves from freeze-damaged leaves. However, the false negatives mainly resulted from asymptomatic-infected leaves being classified as healthy. Analyses of average vegetation indices of freeze-damaged, healthy (non-infected), asymptomatic-infected, and symptomatic-infected leaves indicated that the normalized difference vegetation index and the simple ratio index were statistically different.

Application of Visible-Near Infrared Spectra Technique Based on to the Non-Destructive Measurement of the Salmon Quality

2014 ◽  
Vol 926-930 ◽  
pp. 961-964
Author(s):  
Jiao Jiao Yin
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Principal Component ◽  
Support Vector ◽  
Analysis Method ◽  
Linear Discriminant ◽  
Near Infrared Spectra ◽  
Destructive Measurement ◽  
Non Destructive

Because the reflectivity of astaxanthin vary in different bands (mainly 400nm-600nm), so we use the visible-near infrared spectra technique to irradiate the salmon. Because in daily life, people grade the salmon flesh with a color card. In this paper, we first use principal component analysis to reduce the dimensionality of the spectral data of salmon, then use linear discriminant analysis method, least squares support vector machine classification method to distinguish the flesh quality. The correct classification rates are 60%and73.3%. The results show that we can use visible – near infrared spectra to distinguish the quality of the salmon which doesn’t be dissected.

scholarly journals Characterization of pasteurized milk spoilage by electronic nose in relation to its selected quality parameters

2021 ◽  
Vol 10 (2) ◽  
pp. 383-397
Author(s):  
Saleem Ehsan ◽  
Zahir Al-Attabi ◽  
Nasser Al-Habsi ◽  
Michel R. G. Claereboudt ◽  
Mohammad Shafiur Rahman
Keyword(s):  
Shelf Life ◽  
Electronic Nose ◽  
Principal Component ◽  
Titratable Acidity ◽  
Bacterial Count ◽  
Quality Parameters ◽  
Accurate Estimation ◽  
Total Bacterial Count ◽  
Microbial Count ◽  
Linear Discriminant

Pasteurized fresh milk requires an accurate estimation of shelf life under various conditions to minimize the risk of spoilage and product losses. Milk samples were stored for 56 h in an oven at 25°C and for 15 days in a refrigerator at 4°C. Samples were analyzed using an electronic nose (e-nose), total bacterial count, titratable acidity and pH to determine the quality of milk. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were used to analyze e-nose data of milk stored at 25°C, and 4°C. A clear shift in quality was identified by the e-nose, which also appeared in the total bacterial count after 24 h and 12 days for storage at 25 and 4°C, respectively. On the other hand, titratable acidity exceeded the normal limits of 0.14 % - 0.21 % after 24 h for storage at 25°C (0.247 ± 0.006 %) and after 15 days for storage at 4°C (0.25 ± 0.01 %). If pH was a good indicator of quality for samples stored at 25°C, it showed no clear trends for samples stored at 4°C. Based on the microbial count data and e-nose output, the milk had a shelf life of 0.3 day (i.e. 8 h) when stored at 25°C. Shelf life was extended to 9 days when stored at 4°C.

scholarly journals Rapid Authentication of 100% Italian Durum Wheat Pasta by FT-NIR Spectroscopy Combined with Chemometric Tools

Foods ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1551
Author(s):  
Annalisa De Girolamo ◽  
Salvatore Cervellieri ◽  
Erminia Mancini ◽  
Michelangelo Pascale ◽  
Antonio Francesco Logrieco ◽  
...  
Keyword(s):  
Durum Wheat ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Linear Discriminant ◽  
Production And Consumption ◽  
High Prediction ◽  
Validation Set ◽  
Performance Results ◽  
Prediction Capability

Italy is the country with the largest durum wheat pasta production and consumption. The mandatory labelling for pasta indicating the country of origin of wheat has made consumers more aware about the consumed pasta products and is influencing their choice towards 100% Italian wheat pasta. This aspect highlights the need to promote the use of domestic wheat as well as to develop rapid methodologies for the authentication of pasta. A rapid, inexpensive, and easy-to-use method based on infrared spectroscopy was developed and validated for authenticating pasta made with 100% Italian durum wheat. The study was conducted on pasta marketed in Italy and made with durum wheat cultivated in Italy (n = 176 samples) and on pasta made with mixtures of wheat cultivated in Italy and/or abroad (n = 185 samples). Pasta samples were analyzed by Fourier transform-near infrared (FT-NIR) spectroscopy coupled with supervised classification models. The good performance results of the validation set (sensitivity of 95%, specificity and accuracy of 94%) obtained using principal component-linear discriminant analysis (PC-LDA) clearly demonstrated the high prediction capability of this method and its suitability for authenticating 100% Italian durum wheat pasta. This output is of great interest for both producers of Italian pasta pointing toward authentication purposes of their products and consumer associations aimed to preserve and promote the typicity of Italian products.

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