Array-optimized artificial olfactory sensor enabling cost-effective and non-destructive detection of mycotoxin-contaminated maize.

The MobileNetV3-based improved sine-cosine algorithm (ISCA-MobileNetV3) was combined with an artificial olfactory sensor (AOS) to address the redundancy in olfactory arrays, thereby achieving low-cost and high-precision detection of mycotoxin-contaminated maize. Specifically, volatile organic compounds of maize interacted with unoptimized AOS containing eight porphyrins and eight dye-attached nanocomposites to obtain the scent fingerprints for constructing the initial data set. The optimal decision model was MobileNetV3, with more than 98.5% classification accuracy, and its output training loss would be input into the optimizer ISCA. Remarkably, the number of olfactory arrays was reduced from 16 to 6 by ISCA-MobileNetV3 with about a 1% decrease in classification accuracy. Additionally, the developed system showed that each online evaluation was less than one second on average, demonstrating outstanding real-time performance for ensuring food safety. Therefore, AOS combined with ISCA-MobileNetV3 will encourage the development of an affordable and on-site platform for maize quality detection.

Hydration dominates dielectric spectroscopy-based high accuracy quantification of lactose content in cow's milk.

BACKGROUND: Lactose is a critical factor in the quality of milk and dairy products. Achieving high accuracy and rapid detection of lactose content in cow's milk remains a challenge. Dielectric spectroscopy has emerged as a promising tool for detecting food components. We explored the effect of lactose content on the dielectric spectra of cow's milk and we propose a rapid analytical method for the quantitative determination of lactose content in cow's milk with high accuracy based on dielectric spectra. RESULTS: We obtained the dielectric spectra of 316 cow's milk samples in the frequency range 20-4500 MHz and noticed a strong negative correlation between the lactose content and the value of the dielectric loss factor (ε″) below 1500 MHz. Lactose does not affect cow's milk dielectric properties by excluded volume effect, but dominates the effect on the dielectric properties of cow's milk by hydration. The support vector regression model based on the variable importance in projection has the best prediction performance for lactose content. Its root-mean-square error of prediction set and residual prediction deviation is 0.29 g kg-1 and 6.968, respectively, and its prediction performance is better than that of the currently reported near-infrared (NIR) method and other methods. CONCLUSION: Despite the weak polarity of lactose molecules, its hydration is a significant factor affecting the dielectric properties of milk. The present study provides a basis for high accuracy and rapid quantitative detection of lactose in cow's milk based on dielectric spectra. © 2023 Society of Chemical Industry.

Comparison of near-infrared and dielectric spectra for quantitative identification of bovine colostrum adulterated with mature milk.

The nonhomogeneity of bovine colostrum leads to strong scattering effects for electromagnetic waves, which affects the application of electromagnetic spectroscopy in detecting colostrum. This work aimed to compare the performance of near-infrared spectroscopy (NIRS) and dielectric spectroscopy (DS) in quantitatively predicting the content of mature milk as an adulterant in colostrum. The near-infrared spectra in the range of 833 to 2,500 nm and the dielectric spectra in the range of 20 to 4,500 MHz of 150 adulterated colostrum samples containing 0 to 50% mature milk were analyzed. The different proportions of mature milk in colostrum significantly changed near-infrared and dielectric spectra. The principal component analysis score plot showed that both NIRS and DS could identify the proportion of mature milk in colostrum, but the 2 methods had different characteristics. Linear partial least squares regression and nonlinear least squares support vector machine (LSSVM) models based on near-infrared and dielectric spectra were established to identify doping proportions. The results showed that DS had better identification performance with a root-mean-square error of prediction of 4.9% and a residual prediction deviation of 3.441 by successive projection algorithm LSSVM, whereas NIRS was relatively weak with a root-mean-square error of prediction of 7.3% and a residual prediction deviation of 2.301 by full-spectra LSSVM. This work provides important insights for the quantitative prediction of nonhomogeneous liquid food by DS.

Qualitative identification of mature milk adulteration in bovine colostrum using noise-reduced dielectric spectra and linear model.

BACKGROUND: The rapid and accurate identification of colostrum, a strong non-homogeneous food, remains a challenge. In the present study, the dielectric spectra including the dielectric constant (ε') and loss factor (ε″) of 154 colostrum samples adulterated with 0-50% mature milk were measured from 20 to 4500 MHz. RESULTS: The results showed that the noise-reducing spectral preprocessing, including Savitzky-Golay (S-G), second derivative (SD), and S-G + SD, was significantly better than scattering-eliminating, including standard normal variate (SNV), multiplicative scatter correction (MSC), and SNV + MSC. The combination of S-G and SD was the best. Principal component analysis results demonstrated that dielectric spectroscopy is less susceptible to the inhomogeneity of colostrum and can be used to identify doped colostrum. The identification performance of linear models was better than that of non-linear models. The established linear discriminant analysis model based on full spectra had the best accuracy rates of 99.14% and 97.37% in the calibration and validation sets, respectively. Confirmatory tests on samples from different sources confirmed the satisfactory robustness of the proposed model. CONCLUSION: We found that the main unfavorable effect on the identification based on dielectric spectroscopy was noise interference, rather than scattering effect caused by inhomogeneity of colostrum. The satisfactory results undoubtedly cast light on rapid detection of strongly non-homogeneous foods based on dielectric spectroscopy. © 2022 Society of Chemical Industry.

Evidence that high-migration drug-surviving MOLT4 leukemia cells exhibit cancer stem cell-like properties.

Leukemia represents a spectrum of hematological malignancies threatening human health. Resistance to treatments and metastasis of leukemia are the main causes of death in patients. Leukemia stem cells (LSCs) are the initiating cells of leukemia as well as the main source of drug resistance, invasion and metastasis. Consequently, eliminating LSCs is a prerequisite to eradicate leukemia. Preliminary studies in our laboratory have shown that chemokines and their related receptors play an important role in the drug resistance and metastasis of leukemic cells. In this study, we obtained high migration drug-surviving (short term) MOLT4 cells (hMDSCs-MOLT4) with treatment of doxorubicin (DOX) after Transwell assay. Then we detected stem cell-associated molecular markers on hMDSCs-MOLT4 cells and the parental MOLT4 cells by FCM, QPCR, western blotting, H&E staining and immunohisto-chemistry experimental techniques in vitro and in vivo. Moreover, we explored its impact on drug resistance and tumor formation. Then we found that compared with the parental MOLT4 cells, the mRNA expression levels of stem cell-related factors Sox2, Oct4, C-myc, Klf4, Nanog, Bmi-1, CXCR4 are increased in hMDSCs-MOLT4 cells, together with the protein expression levels of Sox2, Oct4, Klf4, Nanog, CXCR4 and CD34. Our results indicated that hMDSCs-MOLT4 cells exhibited strong drug resistance and certain cancer stem cell-like characteristics. It is the first indication that the targeting stemness factors such as Sox2, Oct4, Klf4, Nanog and CXCR4 may represent plausible options for eliminating T-ALL stem-like cells. The present findings shed light on the relationship between drug-tolerant leukemic cells and cancer stem cells.