| 1. |
- Guo, Zhiming, et al.
(författare)
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Classification for Penicillium expansum Spoilage and Defect in Apples by Electronic Nose Combined with Chemometrics
- 2020
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Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 20:7
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Tidskriftsartikel (refereegranskat)abstract
- It is crucial for the efficacy of the apple storage to apply methods like electronic nose systems for detection and prediction of spoilage or infection by Penicillium expansum. Based on the acquisition of electronic nose signals, selected sensitive feature sensors of spoilage apple and all sensors were analyzed and compared by the recognition effect. Principal component analysis (PCA), principle component analysis-discriminant analysis (PCA-DA), linear discriminant analysis (LDA), partial least squares discriminate analysis (PLS-DA) and K-nearest neighbor (KNN) were used to establish the classification model of apple with different degrees of corruption. PCA-DA has the best prediction, the accuracy of training set and prediction set was 100% and 97.22%, respectively. synergy interval (SI), genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) are three selection methods used to accurately and quickly extract appropriate feature variables, while constructing a PLS model to predict plaque area. Among them, the PLS model with unique variables was optimized by CARS method, and the best prediction result of the area of the rotten apple was obtained. The best results are as follows: Rc = 0.953, root mean square error of calibration (RMSEC) = 1.28, Rp = 0.972, root mean square error of prediction (RMSEP) = 1.01. The results demonstrated that the electronic nose has a potential application in the classification of rotten apples and the quantitative detection of spoilage area.
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| 2. |
- Guo, Zhiming, et al.
(författare)
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Intelligent evaluation of taste constituents and polyphenols-to-amino acids ratio in matcha tea powder using near infrared spectroscopy
- 2021
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Ingår i: Food Chemistry. - : Elsevier. - 0308-8146 .- 1873-7072. ; 353
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Tidskriftsartikel (refereegranskat)abstract
- Matcha tea is rich in taste and bioactive constituents, quality evaluation of matcha tea is important to ensure flavor and efficacy. Near-infrared spectroscopy (NIR) in combination with variable selection algorithms was proposed as a fast and non-destructive method for the quality evaluation of matcha tea. Total polyphenols (TP), free amino acids (FAA), and polyphenols-to-amino acids ratio (TP/FAA) were assessed as the taste quality indicators. Successive projections algorithm (SPA), genetic algorithm (GA), and simulated annealing (SA) were subsequently developed from the synergy interval partial least squares (SiPLS). The overall results revealed that SiPLS-SPA and SiPLS-SA models combined with NIR exhibited higher predictive capabilities for the effective determination of TP, FAA and TP/FAA with correlation coefficient in the prediction set (Rp) of Rp > 0.97, Rp > 0.98 and Rp > 0.98 respectively. Therefore, this simple and efficient technique could be practically exploited for tea quality control assessment.
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| 3. |
- Guo, Zhiming, et al.
(författare)
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Label-free surface enhanced Raman scattering spectroscopy for discrimination and detection of dominant apple spoilage fungus
- 2021
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Ingår i: International Journal of Food Microbiology. - : Elsevier. - 0168-1605 .- 1879-3460. ; 338
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Tidskriftsartikel (refereegranskat)abstract
- Fungal infection is one of the main causes of apple corruption. The main dominant spoilage fungi in causing apple spoilage are storage mainly include Penicillium Paecilomyces paecilomyces (P. paecilomyces), penicillium chrysanthemum (P. chrysogenum), expanded Penicillium expansum (P. expansum), Aspergillus niger (Asp. niger) and Alternaria. In this study, surface-enhanced Raman spectroscopy (SERS) based on gold nanorod (AuNRs) substrate method was developed to collect and examine the Raman fingerprints of dominant apple spoilage fungus spores. Standard normal variable (SNV) was used to pretreat the obtained spectra to improve signal-tonoise ratio. Principal component analysis (PCA) was applied to extract useful spectral information. Linear discriminant analysis (LDA) and non-linear pattern recognition methods including K nearest neighbor (KNN), Support vector machine (SVM) and back propagation artificial neural networks (BPANN) were used to identify fungal species. As the comparison of modeling results shown, the BPANN model established based on the characteristic spectra variables have achieved the satisfactory result with discrimination accuracy of 98.23%; while the PCA-LDA model built using principal component variables achieved the best distinguish result with discrimination accuracy of 98.31%. It was concluded that SERS has the potential to be an inexpensive, rapid and effective method to detect and identify fungal species.
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| 4. |
- Guo, Zhiming, et al.
(författare)
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Nondestructive monitoring storage quality of apples at different temperatures by near-infrared transmittance spectroscopy
- 2020
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Ingår i: Food Science & Nutrition. - : WILEY. - 2048-7177. ; 8:7, s. 3793-3805
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Tidskriftsartikel (refereegranskat)abstract
- Apple is the most widely planted fruit in the world and is popular in consumers because of its rich nutritional value. In this study, the portable near-infrared (NIR) transmittance spectroscopy coupled with temperature compensation and chemometric algorithms was applied to detect the storage quality of apples. The postharvest quality of apples including soluble solids content (SSC), vitamin C (VC), titratable acid (TA), and firmness was evaluated, and the portable spectrometer was used to obtain near-infrared transmittance spectra of apples in the wavelength range of 590-1,200 nm. Mixed temperature compensation method (MTC) was used to reduce the influence of temperature on the models and to improve the adaptability of the models. Then, variable selection methods, such as uninformative variable elimination (UVE), competitive adaptive reweighted sampling (CARS), and successive projections algorithm (SPA), were developed to improve the performance of the models by determining characteristic variables and reducing redundancy. Comparing the full spectral models with the models established on variables selected by different variable selection methods, the CARS combined with partial least squares (PLS) showed the best performance with prediction correlation coefficient (R-p) and residual predictive deviation (RPD) values of 0.9236, 2.604 for SSC; 0.8684, 2.002 for TA; 0.8922, 2.087 for VC; and 0.8207, 1.992 for firmness, respectively. Results showed that NIR transmittance spectroscopy was feasible to detect postharvest quality of apples during storage.
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| 5. |
- Guo, Zhiming, et al.
(författare)
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Quantitative detection of apple watercore and soluble solids content by near infrared transmittance spectroscopy
- 2020
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Ingår i: Journal of Food Engineering. - : ELSEVIER SCI LTD. - 0260-8774 .- 1873-5770. ; 279
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Tidskriftsartikel (refereegranskat)abstract
- Near-infrared (NIR) spectroscopy as an emerging analytical technique was used for the first time to quantitatively detect the watercore degree and soluble solids content (SSC) in apple. To reduce the data processing time and meet the needs of practical application, the variable selection methods including synergy interval (SI), successive projections algorithm (SPA), genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) were used to identify the characteristic variables and simplify the models. The spectral variables closely related to the apple bioactive components were used for the establishment of the partial least squares (PLS) models. The predictive correlation coefficient (R-p), root mean square error of prediction (RMSEP), and residual predictive deviation (RPD) were used to estimate the performance of the models. The CARS-PLS models displayed the best prediction performance using 600-1000 nm spectra with R-p, RMSEP, and RPD values of 0.9562, 1.340% and 3.720 for apple watercore degree; 0.9808, 0.327 (o)Bx and 4.845 for apple SSC, respectively. These results demonstrate the potential of the NIR transmittance spectroscopy technology for quantitative detection of SSC and watercore degree in apple fruit.
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| 6. |
- Guo, Zhiming, et al.
(författare)
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Rapid enrichment detection of patulin and alternariol in apple using surface enhanced Raman spectroscopy with coffee-ring effect
- 2021
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Ingår i: Lebensmittel-Wissenschaft + Technologie. - : Elsevier. - 0023-6438 .- 1096-1127. ; 152
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Tidskriftsartikel (refereegranskat)abstract
- Patulin (PAT) and alternariol (AOH) are the main mycotoxin contaminants in fruits and their products, which have great toxic effects on human body due to their teratogenicity and carcinogenicity. This study proposed a surface enhanced Raman spectroscopy (SERS) technology combining chemometrics and coffee-ring effect to build high-throughput label-free detection models for PAT and AOH. A stable coffee ring structure was built by optimizing the drying temperature and droplet volume. Comparing the partial least squares (PLS) models grounded on variables selection method, the best performance was obtained by using synergy interval (Si) and genetic algorithm (GA) for PAT (R-c = 0.9905, R-p = 0.9759) and AOH (R-c = 0.9829, R-p = 0.9808), respectively. The limits of detection (LOD) for PAT and AOH were as low as 1 mu g L-1, and the recovery rates were 92.80%-114.83% with relative standard deviation (RSD) = 4.86 for PAT and 82.06%-108.13% with RSD <= 2.28% for AOH. The SERS technology combined with chemometrics and coffee-ring effect holds promise for high-throughput label-free detection of PAT and AOH in fruits and their products.
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| 7. |
- Guo, Zhiming, et al.
(författare)
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Simultaneous quantification of active constituents and antioxidant capability of green tea using NIR spectroscopy coupled with swarm intelligence algorithm
- 2020
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Ingår i: Lebensmittel-Wissenschaft + Technologie. - : ELSEVIER. - 0023-6438 .- 1096-1127. ; 129
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Tidskriftsartikel (refereegranskat)abstract
- A simple, rapid and low-cost analytical method was employed for simultaneous determination of bioactive constituents and antioxidant capability of green tea. The strategy was based on swarm intelligence algorithms with partial least squares (PLS) such as simulated annealing PLS (SA-PLS), ant colony optimization PLS (ACO-PLS), genetic algorithm PLS (GA-PLS), and synergy interval PLS (Si-PLS) coupled with Near-infrared (NIR) spectroscopy. These algorithms were independently applied to select informative spectral variables and improve the prediction of green tea components. Results showed that NIR combined with SA-PLS and Si-PLS had a strong correlation coefficient with the wet-chemical methods for predicting epigallocatechin gallate (R-p(2) = 0.97); epigallocatechin (R-p(2) = 0.97); epicatechin gallate (R-p(2) = 0.96); epicatechin (R-p(2) = 0.91); catechin (R-p(2) = 0.98); caffeine (R-p(2) = 0.96); theanine (R-p(2) = 0.93); and antioxidant capability (R-p(2) = 0.80) in green tea. Our results revealed the potential utilization of NIR spectroscopy coupled with SA-PLS and Si-PLS algorithms as an effective and robust technique to simultaneously predict active constituents and antioxidant capability of green tea.
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| 8. |
- Tahir, Haroon Elrasheid, et al.
(författare)
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Authentication of the geographical origin of Roselle (Hibiscus sabdariffa L) using various spectroscopies : NIR, low-field NMR and fluorescence
- 2020
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Ingår i: Food Control. - : ELSEVIER SCI LTD. - 0956-7135 .- 1873-7129. ; 114
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Tidskriftsartikel (refereegranskat)abstract
- Roselle (Hibiscus sabdariffa) quality is strongly influenced by several factors and the geographical origin is one of the key parameters. However, fraudulent practices including mislabeling of the geographical sources might occur. In the present experiment, the analyzed samples consisted of 64 authentic samples originating from the world's best roselle country (Sudan) and eight samples from the world's largest producer (China) were investigated. The study investigated whether near-infrared spectroscopy (NIR), low filed NMR (LF-NMR) spectroscopy and fluorescence spectroscopy can enable roselle geographical origin to be identified. Principal components analysis (PCA), hierarchical cluster analysis (HCA) and PCA combined with linear discriminant analysis (PCA-LDA) were performed on NIR data to assess a possible classification of samples based on origin. Roselle samples from the same geographical areas might group together in the PCA plot. Correct discrimination was achieved by HCA. The classification of the samples into calibration and prediction sets yielded 100% discrimination rates for both calibration and prediction sets. LF-NMR measurement, to detect differences in the relaxation times, indicated that these were affected by the variations in geographical origins. Additionally, the fluorescence spectroscopy spectra presented different shapes and intensity of fluorescence emissions, demonstrating the differences in the samples. This study proved that the three spectroscopies could be viable tools for utilization in classifying roselle samples by their geographical origins.
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| 9. |
- Tahir, Haroon Elrasheid, et al.
(författare)
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Smart films fabricated from natural pigments for measurement of total volatile basic nitrogen (TVB-N) content of meat for freshness evaluation : A systematic review
- 2022
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Ingår i: Food Chemistry. - : Elsevier. - 0308-8146 .- 1873-7072. ; 396
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Forskningsöversikt (refereegranskat)abstract
- Major databases were searched from January 2012 to August 2021 and 54 eligible studies were included in the meta-analysis to estimate the overall mean of total volatile basic nitrogen (TVB-N) in meat. The mean of TVB-N was 24.96 mg/100 g (95 % CI:23.10-26.82). The pooled estimate of naphthoquinone, curcumin, anthocyanins, alizarin and betalains were 25.98 mg/100 g (95 %CI:19.63-32.33), 30.03 mg/100 g (95 %CI: 24.15-35.91), 24.92 mg/100 g (95 %CI: 22.55-27.30), 23.37 mg/100 g (95 %CI:19.42-27.33) and 19.50 mg/100 g (95 % CI:17.87-21.12), respectively. Meanwhile, subgroups based on meat types showed that smart film was most used in aquatic products at 27.19 mg/100 g (95 %CI:24.97-29.42), followed by red meat at 19.69 mg/100 g (95 % CI:17.44-21.94). Furthermore, 4 degrees C was the most storage temperature used for testing the performance of smart films at 25.48 mg/100 g (95 %CI:23.05-27.90), followed by storage at 25 degrees C of 25.65 mg/100 g (95 % CI:22.17-29.13). Substantial heterogeneity was found across the eligible studies (I-2 = 99 %, p = 0.00). The results of the trim-and-fill method demonstrated publication bias was well controlled.
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| 10. |
- Tahir, Haroon Elrasheid, et al.
(författare)
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The use of analytical techniques coupled with chemometrics for tracing the geographical origin of oils : A systematic review (2013-2020)
- 2022
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Ingår i: Food Chemistry. - : Elsevier. - 0308-8146 .- 1873-7072. ; 366
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Forskningsöversikt (refereegranskat)abstract
- The global market for imported, high-quality priced foods has grown dramatically in the last decade, as consumers become more conscious of food originating from around the world. Many countries require the origin label of food to protect consumers need about true characteristics and origin. Regulatory authorities are looking for an extended and updated list of the analytical techniques for verification of authentic oils and to support law implementation. This review aims to introduce the efforts made using various analytical tools in combination with the multivariate analysis for the verification of the geographical origin of oils. The popular analytical tools have been discussed, and scientometric assessment that underlines research trends in geographical authentication and preferred journals used for dissemination has been indicated. Overall, we believe this article will be a good guideline for food industries and food quality control authority to assist in the selection of appropriate methods to authenticate oils.
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