Recent advances in simultaneous detection strategies for multi-mycotoxins in foods.

Mycotoxin contamination has become a challenge in the field of food safety testing, given the increasing emphasis on food safety in recent years. Mycotoxins are widely distributed, in heavily polluted areas. Food contamination with these toxins is difficult to prevent and control. Mycotoxins, as are small-molecule toxic metabolites produced by several species belonging to the genera Aspergillus, Fusarium, and Penicillium growing in food. They are considered teratogenic, carcinogenic, and mutagenic to humans and animals. Food systems are often simultaneously contaminated with multiple mycotoxins. Due to the additive or synergistic toxicological effects caused by the co-existence of multiple mycotoxins, their individual detection requires reliable, accurate, and high-throughput techniques. Currently available, methods for the detection of multiple mycotoxins are mainly based on chromatography, spectroscopy (colorimetry, fluorescence, and surface-enhanced Raman scattering), and electrochemistry. This review provides a comprehensive overview of advances in the multiple detection methods of mycotoxins during the recent 5 years. The principles and features of these techniques are described. The practical applications and challenges associated with assays for multiple detection methods of mycotoxins are summarized. The potential for future development and application is discussed in an effort, to provide standards of references for further research.

Dosimetric feasibility of stereotactic ablative radiotherapy in pulmonary vein isolation for atrial fibrillation using intensity-modulated proton therapy.

PURPOSE: To evaluate dosimetric properties of intensity-modulated proton therapy (IMPT) for simulated treatment planning in patients with atrial fibrillation (AF) targeting left atrial-pulmonary vein junction (LA-PVJ), in comparison with volumetric-modulated arc therapy (VMAT) and helical tomotherapy (TOMO). METHODS: Ten thoracic 4D-CT scans with respiratory motion and one with cardiac motion were used for the study. Ten respiratory 4D-CTs were planned with VMAT, TOMO, and IMPT for simulated AF. Targets at the LA-PVJ were defined as wide-area circumferential ablation line. A single fraction of 25 Gy was prescribed to all plans. The interplay effects from cardiac motion were evaluated based on the cardiac 4D-CT scan. Dose-volume histograms (DVHs) of the ITV and normal tissues were compared. Statistical analysis was evaluated via one-way Repeated-Measures ANOVA and Friedman's test with Bonferroni's multiple comparisons test. RESULTS: The median volume of ITV was 8.72cc. All plans had adequate target coverage (V23.75Gy  ≥ 99%). Compared with VMAT and TOMO, IMPT resulted in significantly lower dose of most normal tissues. For VMAT, TOMO, and IMPT plans, Dmean of the whole heart was 5.52 ± 0.90 Gy, 5.89 ± 0.78 Gy, and 3.01 ± 0.57 Gy (P < 0.001), mean dose of pericardium was 4.74 ± 0.76 Gy, 4.98 ± 0.62 Gy, and 2.59 ± 0.44 Gy (P < 0.001), and D0.03cc of left circumflex artery (LCX) was 13.96 ± 5.45 Gy, 14.34 ± 5.91 Gy, and 8.43 ± 7.24 Gy (P < 0.001), respectively. However, no significant advantage for one technique over the others was observed when examining the D0.03cc of esophagus and main bronchi. CONCLUSIONS: IMPT targeting LA-PVJ for patients with AF has high potential to reduce dose to surrounding tissues compared to VMAT or TOMO. Motion mitigation techniques are critical for a particle-therapy approach.

[Discrimination of Plum Browning with Near Infrared Spectroscopy].

Flesh browning mostly happens in plum fruit during the post-harvest storage period, which is an important factor affecting the storage quality of plum fruits. Traditional methods used to discriminate plum browning involve the destruction of the intact fruit, which are highly subjective and error-prone. Therefore, the near-infrared (NIR) spectroscopy technique was applied to achieve rapid and non-destructive identification of plum browning and non-browning in this paper. The near infrared diffuse reflectance spectroscopy of 124 plum samples were collected in the band number of 4 000~12 500 cm-1. These samples were classified into two groups, browning (n=70) and non-browning (n=54). In order to find a new way to effectively discriminate plum fruits with flesh browning, three qualitative identification methods: the qualitative test, Mahalanobis distances discriminate analysis (DA) and Back Propagation-artificial neural networks (BP-ANN) were used to compare their capacity of recognizing browning plums and non-browning oneswhile the last two approaches were based on the principal component analysis (PCA) method. These results showed that DA and BP-ANN could be used to conctruct effective classification models for identifying plum browning, and the first ten principal components extracted from original spectra were applied as input variables to build DA and BP-ANN models. The optimal method was obtained with BP-ANN, which gained an accuracy of 100% for calibration set and 97.56% for prediction set, and the identification accuracy rate reached 100% and 98.57% for non-browning samples and browning ones, respectively. It could be concluded that NIR spectroscopy technique combined with chemometrics methods has great potential to recognize plums of browning and non-browning rapidly, non-destructively and effectively.

[Discrimination of donkey meat by NIR and chemometrics].

Donkey meat samples (n = 167) from different parts of donkey body (neck, costalia, rump, and tendon), beef (n = 47), pork (n = 51) and mutton (n = 32) samples were used to establish near-infrared reflectance spectroscopy (NIR) classification models in the spectra range of 4,000~12,500 cm(-1). The accuracies of classification models constructed by Mahalanobis distances analysis, soft independent modeling of class analogy (SIMCA) and least squares-support vector machine (LS-SVM), respectively combined with pretreatment of Savitzky-Golay smooth (5, 15 and 25 points) and derivative (first and second), multiplicative scatter correction and standard normal variate, were compared. The optimal models for intact samples were obtained by Mahalanobis distances analysis with the first 11 principal components (PCs) from original spectra as inputs and by LS-SVM with the first 6 PCs as inputs, and correctly classified 100% of calibration set and 98. 96% of prediction set. For minced samples of 7 mm diameter the optimal result was attained by LS-SVM with the first 5 PCs from original spectra as inputs, which gained an accuracy of 100% for calibration and 97.53% for prediction. For minced diameter of 5 mm SIMCA model with the first 8 PCs from original spectra as inputs correctly classified 100% of calibration and prediction. And for minced diameter of 3 mm Mahalanobis distances analysis and SIMCA models both achieved 100% accuracy for calibration and prediction respectively with the first 7 and 9 PCs from original spectra as inputs. And in these models, donkey meat samples were all correctly classified with 100% either in calibration or prediction. The results show that it is feasible that NIR with chemometrics methods is used to discriminate donkey meat from the else meat.

[Nondestructive discrimination of strawberry varieties by NIR and BP-ANN].

Strawberry variety is a main factor that can influence strawberry fruit quality. The use of near-infrared reflectance spectroscopy was explored discriminate among samples of strawberry of different varieties. And the significance of difference among different varieties was analyzed by comparison of the chemical composition of the different varieties samples. The performance of models established using back propagation-artificial neural networks (BP-ANN), least squares-support vector machine and discriminant analysis were evaluated on spectra range of 4545-9090 cm(-1). The optimal model was obtained by BP-ANN with a topology of 12-18-3, which correctly classified 96.68% of calibration set and 97.14% of prediction set. And the 94.95%, 97% and 98.29% classifications were given respectively for "Tianbao" (n=99), "Fengxiang" (n=100) and "Mingxing" (n=117). One-way analysis of variance was made for comparison of the mean values for soluble solids content (SSC), titratable acid (TA), pH value and SSC-TA ratio, and the statistically significant differences were found. Principal component analysis was performed on the four chemical compositions, and obvious clustering tendencies for different varieties were found. These results showed that NIR combined with BP-ANN can discriminate strawberry of different varieties effectively, and the difference in chemical compositions of different varieties strawberry might be a chemical validation for NIR results.