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14. Supplemental Material - Before reliable near infrared spectroscopic analysis - the critical sampling proviso. Part 1. Generalised theory of sampling

Author

Esbensen, Kim H and Abu-Khalaf, Nawaf

Subjects
FOS: Materials engineering, 91299 Materials Engineering not elsewhere classified
Abstract

Supplemental Material for Before reliable near infrared spectroscopic analysis - the critical sampling proviso. Part 1. Generalised theory of sampling by Kim H Esbensen and Nawaf Abu-Khalaf in Journal of Near Infrared Spectroscopy

Published
2022
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27. VISIBLE/NEAR-INFRARED (VIS/NIR) SPECTROSCOPY TECHNIQUE TO DETECT GRAY MOLD DISEASE IN THE EARLY STAGES OF TOMATO FRUIT.

Author

Njjar, Khadija and Abu-Khalaf, Nawaf

Subjects
*DISEASE progression, *PRINCIPAL components analysis, *NEAR infrared spectroscopy, *EARLY diagnosis, *FRUIT
Abstract

Fungal diseases are among the most common problems that affect tomato fruits, especially after harvesting. The early detection of these diseases may save 60% of the losses. Thus, this study aimed to detect the gray mold disease at an early stage on tomato fruits using the non-destructive visible/near-infrared (VIS/NIR) technique. Botrytis. cinerea fungus was isolated and identified using two specific primer sets (C729+/- and BC108+/BC563-). Three tomato varieties were used for this study (i.e. Harver, Izmer and Ekram), 30 samples from each variety, 20 samples were injected with the B. cinerea and 10 samples were left as control/healthy samples. Samples were examined using the VIS/NIR spectroscopy with a range of 550-1100 nm for three days. Thereafter, the data were analyzed using principal component analysis (PCA), where the results showed the ability of VIS/NIR to detect the infected samples on the second day of the injection, and before the symptoms appear on the samples. Two PCs explained 99% and 100% of VIS/NIR variance, respectively. [ABSTRACT FROM AUTHOR]

Published
2021
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33. Evaluating the potential use of electronic tongue in early identification and diagnosis of bacterial infections.

Author

Ramahi, Rowa' Al, Zaid, Abdel Naser, and Abu-Khalaf, Nawaf

Subjects
ELECTRONIC tongues, BACTERIAL diseases, EARLY diagnosis, MULTIPLE correspondence analysis (Statistics), BACTERIAL typing, DETECTORS
Abstract

Background: Electronic tongue (ET) is a simple device that may have some applications in the medical field as an alternative tool to traditional diagnostic methods. The aim of this study is to evaluate the potential use and accuracy of ET in the diagnosis of certain bacterial infections. Methods: An alpha-Astree ET was used for the detection of known bacterial strains Staphylococcus aureus (ATCC 25923), Escherichia coli (ATCC 25922), and Pseudomonas aeruginosa (ATCC 27853) which were tested at three-time intervals; 15, 18, and 24 hrs (hr). This was used to build a qualitative and quantitative mathematical model to detect the presence of bacteria at the earliest possible time. When the model is robust, new unknown samples were tested after 15 hrs of bacterial growth. Samples were identified using multivariate data analysis techniques. Results: Principal Component Analysis (PCA) scores showed that ET can distinguish between the three bacteria at different times 15, 18, and 24 hrs using different sensors. In the PCA scores plots, the discrimination index was 83% at 15 hrs, 88% at 18 hrs, and 96% at 24 hrs, the variances explained by the two principal components were 84%, 99%, and 97% at 15, 18, and 24 hrs, respectively. Fifteen hours was the earliest time at which the bacteria could be detected. Then six samples of E.coli (as unknown samples) were tested after 15 hrs of inoculation, the two discrimination function explained about 100% of the variance (ie, 79.7+22.3%) and all unknown samples were identified as E.coli. Conclusion: ET could differentiate between types of bacteria in addition to identifying unknown bacterial cultures (E. coli) at times shorter than that required in the current culture-based methods (24–48 hrs), this could be of a great value in early diagnosis of life-threatening infections. [ABSTRACT FROM AUTHOR]

Published
2019
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47. Predicting the quality traits of white striped turkey breast by visible/near infra-red spectroscopy and multivariate data analysis

Author

Samer Mudalal, Nawaf Abu-Khalaf, Amal Zaid, Massimiliano Petracci, Mudalal, Samer, Zaid, Amal, Abu-Khalaf, Nawaf, and Petracci, Massimiliano

Subjects
Near infra red spectroscopy, White (horse), Multivariate analysis, 040301 veterinary sciences, Vis nir spectroscopy, 0402 animal and dairy science, food and beverages, 04 agricultural and veterinary sciences, pls, Biology, SF1-1100, 040201 dairy & animal science, Animal culture, 0403 veterinary science, VIS/NIR spectroscopy, striping, quality, PLS subject, vis/nir spectroscopy, quality, Animal Science and Zoology, striping, Remote sensing
Abstract

The appearance of white striations over the breast of chicken and turkey meat is considered as a recent emerging and growing problem. The aim of this research is to investigate the ability of visible-near infra-red (VIS/NIR) spectroscopy to predict the quality traits of different levels of white striping (thickness of white striations, moderate < 1 mm and severe ≥ 1 mm) defects in turkey breast muscle. Accordingly, 36 turkey breast fillets affected by different level of white striping defects (normal, moderate and severe) were selected from 20-wk old tom turkeys. Colour traits (L*, a* and b*), pH, marinade uptake, drip loss, cooking loss, and chemical composition (moisture, fat, protein and ash) have been evaluated. Our findings showed that prediction models using partial least squares (PLS) were good for colour traits (a* for example; RPD values were 3.22 and 1.27, R2P were 0.91 and 0.57 while RER values were 11.8 and 3.12) , pH (RPD values were 5.00 and 0.01, R2P were 0.95 and 0.07 while RER values were –1.00 and 15.50), and chemical composition (protein content for instance, the prediction values were as the following: RPD values were 1.93 and 0.79, R2P were 0.80 and 0.34 and then RER were 8.48 and 3.80) in particular for normal and severe white striped meat respectively. In conclusion, the results of this research showed that VIS/NIR spectroscopy prediction models were satisfactory to predict the quality traits in the majority of cases.HIGHLIGHTS White striping is a recent muscle abnormality that affects adversely poultry meat. Visible-near infra-red (VIS/NIR) spectroscopy was used to predict the quality traits of turkey breast meat affected by white striping. Partial least squares (PLS) was good indicator to predict several quality traits of normal meat.

Published
2020

48. Differentiation between Normal and White Striped Turkey Breasts by Visible/Near Infrared Spectroscopy and Multivariate Data Analysis

Author

Samer Mudalal, Nawaf Abu-Khalaf, Massimiliano Petracci, Amal Zaid, Zaid, Amal, Abu-Khalaf, Nawaf, Mudalal, Samer, and Petracci, Massimiliano

Subjects
PCA, white striping, Visible near infrared, Near-infrared spectroscopy, Vis nir spectroscopy, 0402 animal and dairy science, technology, industry, and agriculture, food and beverages, 04 agricultural and veterinary sciences, macromolecular substances, Biology, 040201 dairy & animal science, Reflectivity, humanities, Article, 03 medical and health sciences, VIS/NIR spectroscopy, 0302 clinical medicine, quality, Principal component analysis, 030221 ophthalmology & optometry, Animal Science and Zoology, Food science, Spectroscopy, Food Science
Abstract

The appearance of white striations over breast meat is an emerging and growing problem. The main purpose of this study was to employ the reflectance of visible-near infrared (VIS/NIR) spectroscopy to differentiate between normal and white striped turkey breasts. Accordingly, 34 turkey breast fillets were selected representing a different level of white striping (WS) defects (normal, moderate and severe). The findings of VIS/NIR were analyzed by principal component (PC1) analysis (PCA). It was found that the first PC1 for VIS, NIR and VIS/NIR region explained 98%, 97%, and 96% of the total variation, respectively. PCA showed high performance to differentiate normal meat from abnormal meat (moderate and severe WS). In conclusion, the results of this research showed that VIS/NIR spectroscopy was satisfactory to differentiate normal from severe WS turkey fillets by using several quality traits.

Published
2019

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