Unlocking the Secrets of Terminalia Kernels Using Near-Infrared Spectroscopy

2021 ◽  
pp. 000370282199213
Author(s):  
Eshetu Bobasa ◽  
Michael Netzel ◽  
Anh Dao Thi Phan ◽  
Heather Smyth ◽  
Yasmina Sultanbawa ◽  
...  
Keyword(s):  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Near Infrared Reflectance ◽  
Classification Rate ◽  
Potential Health ◽  
Wide Range ◽  
Fruit Samples ◽  
The Fourier Transform ◽  
Kakadu Plum

In recent years, the native food industry in Australia has increased in both value and volume due to the discovery of a wide range of phytochemicals (e.g., vitamin C, polyphenols) that have potential health benefits. Thus, plant organs and tissues of these native plants are used in a wide range of applications. In particular, the kernel of a native plum, the Kakadu plum ( Terminalia ferdinandiana, Combretaceae) is considered to be rich in lipids and other phytochemical compounds. The aim of this study was to evaluate the use of NIR spectroscopy to analyze and characterize kernel samples and tissues of wild harvest fruit samples. The Fourier transform near-infrared reflectance spectra of cracked kernels, seeds cover tissues, and dry powder Kakadu plum kernels were acquired. Both principal component analysis and partial least squares discriminant analysis were used to analyze and interpret the spectral data. A correct classification rate of 93%, 86%, and 80% was achieved for the identification of kernel provenance using all tissues, seed coats, and the whole nuts, respectively. The results of this study reported for the first time the analysis of Kakadu plum kernels and their tissues using NIR spectroscopy.

scholarly journals The Use of a Micro Near Infrared Portable Instrument to Predict Bioactive Compounds in a Wild Harvested Fruit—Kakadu Plum (Terminalia ferdinandiana)

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1413
Author(s):  
Eshetu Bobasa ◽  
Anh Dao T. Phan ◽  
Michael Netzel ◽  
Heather E. Smyth ◽  
Yasmina Sultanbawa ◽  
...  
Keyword(s):  
Vitamin C ◽  
Bioactive Compounds ◽  
Ellagic Acid ◽  
Near Infrared ◽  
Wide Range ◽  
Fruit Samples ◽  
Nutritional Characteristics ◽  
Analytical Quantification ◽  
Kakadu Plum ◽  
Terminalia Ferdinandiana

Kakadu plum (KP; Terminalia ferdinandiana Exell, Combretaceae) is an emergent indigenous fruit originating from Northern Australia, with valuable health and nutritional characteristics and properties (e.g., high levels of vitamin C and ellagic acid). In recent years, the utilization of handheld NIR instruments has allowed for the in situ quantification of a wide range of bioactive compounds in fruit and vegetables. The objective of this study was to evaluate the ability of a handheld NIR spectrophotometer to measure vitamin C and ellagic acid in wild harvested KP fruit samples. Whole and pureed fruit samples were collected from two locations in the Kimberley region (Western Australia, Australia) and were analysed using both reference and NIR methods. The standard error in cross validation (SECV) and the residual predictive deviation (RPD) values were 1.81% dry matter (DM) with an RPD of 2.1, and 3.8 mg g−1 DM with an RPD of 1.9 for the prediction of vitamin C and ellagic acid, respectively, in whole KP fruit. The SECV and RPD values were 1.73% DM with an RPD of 2.2, and 5.6 mg g−1 DM with an RPD of 1.3 for the prediction of vitamin C and ellagic acid, respectively, in powdered KP samples. The results of this study demonstrated the ability of a handheld NIR instrument to predict vitamin C and ellagic acid in whole and pureed KP fruit samples. Although the RPD values obtained were not considered adequate to quantify these bioactive compounds (e.g., analytical quantification), this technique can be used as a rapid tool to screen vitamin C in KP fruit samples for high and low quality vitamin C.

Lighting the Ivory Track: Are Near-Infrared and Chemometrics Up to the Job? A Proof of Concept

2019 ◽  
Vol 73 (7) ◽  
pp. 816-822 ◽  
Author(s):  
Aoife Power ◽  
Sandy Ingleby ◽  
James Chapman ◽  
Daniel Cozzolino
Keyword(s):  
Near Infrared ◽  
Mammalian Species ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Proof Of Concept ◽  
Classification Rate ◽  
Complementary Method ◽  
Infrared Reflection ◽  
Rapid Tool

A rapid tool to discriminate rhino horn and ivory samples from different mammalian species based on the combination of near-infrared reflection (NIR) spectroscopy and chemometrics was evaluated. In this study, samples from the Australian Museum mammalogy collection were scanned between 950 nm and 1650 nm using a handheld spectrophotometer and analyzed using principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). An overall correct classification rate of 73.5% was obtained for the classification of all samples. This study demonstrates the potential of NIR spectroscopy coupled with chemometrics as a means of a rapid, nondestructive classification technique of horn and ivory samples sourced from a museum. Near-infrared spectroscopy can be used as an alternative or complementary method in the detection of horn and ivory assisting in the combat of illegal trade and aiding the preservation of at-risk species.

scholarly journals Study of the Influence of Different Yeast Strains on Red Wine Fermentation with NIR Spectroscopy and Principal Component Analysis

J ◽  
2018 ◽  
Vol 1 (1) ◽  
pp. 133-147 ◽  
Author(s):  
Antonio Marsico ◽  
Rocco Perniola ◽  
Maria Cardone ◽  
Matteo Velenosi ◽  
Donato Antonacci ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Component Analysis ◽  
Sensory Properties ◽  
Mixed Fermentation ◽  
Wine Production ◽  
Yeast Cultures ◽  
Wide Range

Alcoholic fermentation is a key step in wine production. Indeed, a wide range of compounds, which strongly affect the sensory properties of wine, is produced during this process. While Saccharomyces cerevisiae yeast cultures are commonly employed in winemaking to carry on the fermentation process, some non-Saccharomyces species have recently gained attention due to their ability to produce various metabolites of oenological interest. The use of different yeasts strains usually results in wines with different sensory properties, despite being obtained from the same grape variety. In this paper, we tested the feasibility of using near-infrared spectroscopy (NIR) to discriminate among red wines from three different grape varieties produced with pure S. cerevisiae or by mixed fermentation with a promising non-Saccharomyces yeast, namely the Starmeriella bacillaris, which usually yields wines with significant amounts of glycerol and low levels of ethanol, acetic acid, and acetaldehyde. A principal component analysis (PCA) performed on the NIR spectra was used to search for differences in the samples. The NIR results have been compared with both basic wine parameters and sensory analysis data.

Determination of Moisture in Cheddar Cheese by Near Infrared Reflectance Spectroscopy

1988 ◽  
Vol 71 (3) ◽  
pp. 571-574 ◽  
Author(s):  
Randy L Wehling ◽  
Michelle M Pierce
Keyword(s):  
Near Infrared ◽  
Spectral Response ◽  
Linear Regression Analysis ◽  
Nir Spectroscopy ◽  
Cheddar Cheese ◽  
Multiple Linear Regression Analysis ◽  
Infrared Reflectance ◽  
Near Infrared Reflectance ◽  
Wide Range ◽  
Validation Set

Abstract Near infrared reflectance (NIR) spectroscopy was used to determine the moisture content of Cheddar cheese. Through multiple linear regression analysis, a 3-wavelength calibration was developed for use with a commercial filter monochromator instrument. For a validation set of 47 samples, the correlation coefficient squared (r2) between the NIR and oven moisture methods was 0.92, with a standard error of performance (SEP) of 0.38%. Sample temperature was found to significantly affect the spectral response; therefore, it was necessary to equilibrate all samples to a uniform temperature prior to NIR analysis. Aging may also affect the NIR characteristics of cheese, although it was possible to develop a successful calibration that encompassed a wide range of aging times

Near Infrared Reflectance Spectroscopy in the Prediction of Sensory Properties of Beef

1995 ◽  
Vol 3 (2) ◽  
pp. 81-87 ◽  
Author(s):  
K.I. Hildrum ◽  
T. Isaksson ◽  
T. Næs ◽  
B.N. Nilsen ◽  
M. Rødbotten ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
Near Infrared ◽  
Correlation Coefficients ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Near Infrared Reflectance ◽  
Multivariate Correlation ◽  
Heat Treated ◽  
Shear Press ◽  
Reflectance Measurements

Near infrared (NIR) spectroscopy in the prediction of sensory hardness, tenderness and juiciness of bovine M. Longissimus dorsi muscles has been studied. Principal component regressions (PCR) of sensory variables from NIR reflectance measurements on frozen/thawed beef of 120 heat treated samples yielded multivariate correlation coefficients of cross-validation of 0.74, 0.70 and 0.61 for hardness, tenderness and juiciness, respectively. The corresponding correlation coefficients for NIR measurements of fresh (non-frozen) samples were approximately 0.1 units lower for all sensory variables. Predicting Warner Bratzler (WB) shear press values from NIR measurements gave a correlation coefficient similar to that for prediction of sensory hardness. The univariate correlation coefficient between sensory hardness and WB shear press values was 0.90.

scholarly journals Non-Destructive Classification of Fruits Based on Vis-nir Spectroscopy and Principal Component Analysis

2019 ◽  
Vol 4 (1) ◽  
pp. 89-95 ◽  
Author(s):  
Kusumiyati Kusumiyati ◽  
Yuda Hadiwijaya ◽  
Ine Elisa Putri
Keyword(s):  
Principal Component Analysis ◽  
Fruit Quality ◽  
Near Infrared ◽  
Chemical Properties ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Component Analysis ◽  
Internal Quality ◽  
Fruit Samples ◽  
Non Destructive

Fruits are one of the sources of nutrition needed for health. Fruit quality is generally assessed by physical and chemical properties. Measurement of fruit internal quality is usually done by destructive techniques. Ultraviolet, visible and near-infrared (UV-Vis-NIR) spec-troscopy is a non-destructive technique to measure fruit quality. This technique can rapidly measure the fruit quality, the measured fruit still remains intact, and can be marketed. Besides, UV-Vis-NIR spectrosco-py can also be used to classify fruits. The study aimed to classify var-ious types of fruits using UV-Vis-NIR spectroscopy with wavelengths of 300-1041 nm and Principal Component Analysis (PCA). First de-rivative savitzky-golay with 9 smoothing points (dg1) and multiplica-tive scatter correction (MSC) were applied to correct the spectra. The results showed that the use of uv-vis-nir spectroscopy and PCA com-bined with spectra pre-treatment of the MSC method were able to clas-sify various types of fruits with 100% success rate in all fruit samples including sapodilla, ridge gourd, mango, guava, apple and zucchini. 

Multivariate Classification of Sound and Insect-Infested Seeds of a Tropical Multipurpose Tree, Cordia Africana, with near Infrared Reflectance Spectroscopy

2002 ◽  
Vol 10 (1) ◽  
pp. 45-51 ◽  
Author(s):  
Mulualem Tigabu ◽  
Per Christer Odén
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Calibration Model ◽  
Least Squares Regression ◽  
Near Infrared Reflectance ◽  
Classification Rate ◽  
Multipurpose Tree ◽  
Cordia Africana

Near infrared (NIR) spectroscopy was used to classify insect-infested and sound seeds of a tropical multipurpose tree, Cordia africana Lam. A calibration model derived by partial least squares regression of orthogonal signal corrected spectra resulted in a 100% classification rate. Difference spectrum and partial least squares weight indicated that absorbance differences between insect-infested and sound seeds might have been due to differences in composition of chitin and cuticular lipid components as well as moisture content. The result shows the possibility of using NIR spectroscopy in the seed cleaning process in the future provided that appropriate sorting instruments are developed.

Sensing tomato’s pathogen using Visible/Near infrared (VIS/NIR) spectroscopy and multivariate data analysis (MVDA)

2015 ◽  
Vol 3 (1) ◽  
pp. 12-22
Author(s):  
Nawaf Abu-Khalaf
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Near Infrared ◽  
Multivariate Data ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Sensor Technology ◽  
Support Vector ◽  
Wide Range

Quality of agricultural products is a very important issue for consumers as well as for farmers in relation to price, health and flavour. One of the factors that determine the quality is the absence of pathogens that can cause diseases for products and also for consumers. An advanced method to sense pathogens and their antagonists is the use of Visible/Near Infrared (VIS/NIR) spectroscopy. In this paper, the VIS/NIR spectroscopy, with the help of two techniques of multivariate data analysis (MVDA); namely principal component analysis (PCA) and support vector machine (SVM)-classification; showed very reliable results for sensing two artificially inoculated fungi (Fusarium oxysporum f. sp. Lycopersici and Rhizoctonia solani), and two antagonistic bacteria (Bacillus atrophaeus and Pseudomonas aeruginosa). The two fungi cause loss of quality and quantity for tomatoes. The results showed that the lowest classification rates using VIS/NIR spectroscopy for pathogens, antagonistic and their combinations were 90%, 85% and 74%, respectively. These results open a wide range for using VIS/NIR spectroscopy sensor technology for agricultural commodities quality at quality control checkpoints.

Sensing tomato’s pathogen using Visible/Near infrared (VIS/NIR) spectroscopy and multivariate data analysis (MVDA)

2015 ◽  
Vol 3 (1) ◽  
pp. 12-22
Author(s):  
Nawaf Abu-Khalaf
Keyword(s):  
Data Analysis ◽  
Multivariate Data Analysis ◽  
Near Infrared ◽  
Multivariate Data ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Sensor Technology ◽  
Support Vector ◽  
Wide Range

Quality of agricultural products is a very important issue for consumers as well as for farmers in relation to price, health and flavour. One of the factors that determine the quality is the absence of pathogens that can cause diseases for products and also for consumers. An advanced method to sense pathogens and their antagonists is the use of Visible/Near Infrared (VIS/NIR) spectroscopy. In this paper, the VIS/NIR spectroscopy, with the help of two techniques of multivariate data analysis (MVDA); namely principal component analysis (PCA) and support vector machine (SVM)-classification; showed very reliable results for sensing two artificially inoculated fungi (Fusarium oxysporum f. sp. Lycopersici and Rhizoctonia solani), and two antagonistic bacteria (Bacillus atrophaeus and Pseudomonas aeruginosa). The two fungi cause loss of quality and quantity for tomatoes. The results showed that the lowest classification rates using VIS/NIR spectroscopy for pathogens, antagonistic and their combinations were 90%, 85% and 74%, respectively. These results open a wide range for using VIS/NIR spectroscopy sensor technology for agricultural commodities quality at quality control checkpoints.

Research on the Variety Discrimination of Apple Using a Hybrid Possibilistic Clustering

2013 ◽  
Vol 710 ◽  
pp. 768-771 ◽  
Author(s):  
Xiao Hong Wu ◽  
Tong Xiang Cai ◽  
Bin Wu ◽  
Jun Sun
Keyword(s):  
Clustering Algorithm ◽  
Near Infrared ◽  
Learning Algorithm ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Simple Computation ◽  
Near Infrared Reflectance ◽  
Linear Discriminant ◽  
Possibilistic Clustering ◽  
Discriminant Information

Near infrared reflectance (NIR) spectroscopy has been used to obtain NIR spectra of two varieties of apple samples. The dimensionality of NIR spectra was reduced by principal component analysis (PCA), and discriminant information was extracted by linear discriminant analysis (LDA). Last, a hybrid possibilistic clustering algorithm (HPCA) was utilized as classifier to discriminate the apple samples of different varieties. HPCA integrates possibilistic clustering algorithm (PCA) and improved possibilistic c-means (IPCM) clustering algorithm, and produces not only the membership values but also typicality values by simple computation of the sample co-variance. Experimental results showed that HPCA, as an unsupervised learning algorithm, could quickly and easily discriminate the apple varieties.

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