Siamese Networks for Clinically Relevant Bacteria Classification Based on Raman Spectroscopy.

Identifying bacterial strains is essential in microbiology for various practical applications, such as disease diagnosis and quality monitoring of food and water. Classical machine learning algorithms have been utilized to identify bacteria based on their Raman spectra. However, convolutional neural networks (CNNs) offer higher classification accuracy, but they require extensive training sets and retraining of previous untrained class targets can be costly and time-consuming. Siamese networks have emerged as a promising solution. They are composed of two CNNs with the same structure and a final network that acts as a distance metric, converting the classification problem into a similarity problem. Classical machine learning approaches, shallow and deep CNNs, and two Siamese network variants were tailored and tested on Raman spectral datasets of bacteria. The methods were evaluated based on mean sensitivity, training time, prediction time, and the number of parameters. In this comparison, Siamese-model2 achieved the highest mean sensitivity of 83.61 ± 4.73 and demonstrated remarkable performance in handling unbalanced and limited data scenarios, achieving a prediction accuracy of 73%. Therefore, the choice of model depends on the specific trade-off between accuracy, (prediction/training) time, and resources for the particular application. Classical machine learning models and shallow CNN models may be more suitable if time and computational resources are a concern. Siamese networks are a good choice for small datasets and CNN for extensive data.

Illuminating the Tiny World: A Navigation Guide for Proper Raman Studies on Microorganisms.

Raman spectroscopy is an emerging method for the identification of bacteria. Nevertheless, a lot of different parameters need to be considered to establish a reliable database capable of identifying real-world samples such as medical or environmental probes. In this review, the establishment of such reliable databases with the proper design in microbiological Raman studies is demonstrated, shining a light into all the parts that require attention. Aspects such as the strain selection, sample preparation and isolation requirements, the phenotypic influence, measurement strategies, as well as the statistical approaches for discrimination of bacteria, are presented. Furthermore, the influence of these aspects on spectra quality, result accuracy, and read-out are discussed. The aim of this review is to serve as a guide for the design of microbiological Raman studies that can support the establishment of this method in different fields.

A Paper-Based Biomimetic Sensing Device for the Discrimination of Original and Fraudulent Cigarette Brands Using Mixtures of MoS2 Quantum Dots and Organic Dyes.

In this study, we investigated the combined effects of MoS2 QDs' catalytic properties and the colorimetric responses of organic reagents to create a sniffing device based on the sensor array concept of the mammalian olfactory system. The aim was to differentiate the volatile organic compounds (VOCs) present in cigarette smoke. The designed optical nose device was utilized for the classification of various cigarette VOCs. Unsupervised Principal Component Analysis (PCA) and supervised Linear Discriminant Analysis (LDA) methods were employed for data analysis. The LDA analysis showed promising results, with 100% accuracy in both training and cross-validation. To validate the sensor's performance, we assessed its ability to discriminate between five cigarette brands, achieving 100% accuracy in the training set and 82% in the cross-validation set. Additionally, we focused on studying four popular Iranian cigarette brands (Bahman Kootah, Omega, Montana Gold, and Williams), including fraudulent samples. Impressively, the developed sensor array achieved a perfect 100% accuracy in distinguishing these brands and detecting fraud. We further analyzed a total of 126 cigarette samples, including both original and fraudulent ones, using LDA with a matrix size of (126 × 27). The resulting LDA model demonstrated an accuracy of 98%. Our proposed analytical procedure is characterized by its efficiency, affordability, user-friendliness, and reliability. The selectivity exhibited by the developed sensor array positions it as a valuable tool for differentiating between original and counterfeit cigarettes, thus aiding in border control efforts worldwide.

Investigating the effect of different pre-treatment methods on Raman spectra recorded with different excitation wavelengths.

Raman reference libraries can be used for identification of components in unknown samples as Raman spectroscopy offers fingerprint information of the measured samples. Since Raman libraries often contain many different and/or highly similar spectra, it is important that the spectra are a reliable fingerprint for each compound. However, Raman spectra are highly sensitive to the experimental conditions, and the Raman spectra will change in different conditions even though the same sample is measured. Raman data pre-treatment minimizes the differences between Raman spectra arising from different experimental conditions. In this study, different combinations of pre-treatment methods are used to quantify the effect of each pre-treatment step in minimizing the differences between Raman spectra of the same sample in different experimental conditions, e.g., different excitation wavelengths. These different pre-treatment processes are evaluated for six solvents. The spectra differences between spectra recorded with three excitation wavelengths (532 nm, 633 nm, and 830 nm) are evaluated by angular difference index and the influence on a classification model is tested. The angular difference index of each spectrum after every data pre-treatment step shows a decreasing behavior. It could be demonstrated that wavenumber calibration has the largest effect on the differences between the Raman spectra. However, ω4 correction doesn't have a significate effect in this dataset. The classification results show that the prediction accuracy is improving by doing data pre-treatment. In the dataset obtained in 633 nm a lower amount of pre-treatment steps is needed but in the dataset 830 nm more pre-treatment steps are needed for a high accuracy. The result shows that the choice of an optimal pre-treatment method or combination of methods strongly influences the analysis results, but is far from straightforward, since it depends on the characteristics of the data set and the goal of data analysis.

A facile and eco-friendly fluorometric method for the determination of methotrexate and folic acid in biological samples based on hollow luminescent carbon dots and chemometrics method.

A rapid, simple, and inexpensive spectrofluorimetric sensor has been developed for the simultaneous determination of methotrexate (MTX) and folic acid (FA) based on their interactions with hollow carbon dots (HCDs). Since the use of folic acid to cope with the toxic side effects of MTX in patients is essential, the simultaneous determination of these two compounds has been interesting. The results showed that  MTX could quench the fluorescence of HCDs with a dynamic quenching mechanism. The sensor exhibited a linear concentration range of 1.0 × 10-6-1.9 × 10-4 mol L-1 for MTX and 1.5 × 10-5-9.4 × 10-4 mol L-1 for FA and the obtained detection limits for MTX and FA were 1.6 × 10-7 and 5.0 × 10-7 mol L-1, respectively. The applicability of the method was investigated in the analysis of the urine samples and the partial least squares (PLS) method was used for the simultaneous determination of MTX and FA.

Converting electronic nose into opto-electronic nose by mixing MoS2 quantum dots with organic reagents: Application to recognition of aldehydes and ketones and determination of formaldehyde in milk.

A new colorimetric sensor array based on mixing of Molybdenum disulfide quantum dots (MoS2 QDs) and organic reagents is introduced in this study. MoS2 QDs shows a specific and higher affinity to oxygen functionalized volatile compounds like aldehydes and ketones. Therefore, this designed sensor array is used for classification of eight different aldehydes and ketones based on Linear Discriminate Analysis (LDA) at first. The classification accuracy of 96% and 83% was obtained for training and prediction phases, respectively. Then the introduced colorimetric sensor array is used for the semi-quantitative and quantitative analysis of formaldehyde in milk samples. Formaldehyde is an adulteration that is added to the milk for increasing the storage time. Cow milk samples were provided directly from dairy farmer and from supermarkets and were spiked by formaldehyde in the concentration range of 1-25 ppm. The response of sensor array to these samples were analyzed by partial least squares regression (PLS-R) method and were calibrated for concentration of formaldehyde. The PLSR results (R2 = 0.94 and RMSEC = 2.36) shows that proposed sensor is useable in direct analysis of formaldehyde in milk as a complex matrix.

Reaction rate ambiguities for perturbed spectroscopic data: Theory and implementation.

The analysis of reaction systems and their kinetic modeling is important for both exploratory research and process design. Multivariate curve resolution (MCR) methods are state-of-the-art tools for the analysis of spectral series, but are also affected by an unavoidable solution ambiguity that impacts the obtained concentration profiles, spectra and model parameters. These uncertainties depend on the underlying model and the magnitude of the measurement perturbations. We present a general theoretical approach together with a computational method for the analysis of the solution ambiguity underlying arbitrary kinetic models. The main idea is to determine all those model parameters for which the corresponding pure component factorizations satisfy all given constraints within small error tolerances. This makes it possible to determine bands of concentration profiles and spectra that reflect the underlying ambiguity and circumscribes the potential reliability of MCR solutions. False conclusions on the uniqueness of a solution can be prevented. The procedure can be applied as a post-processing step to MCR methods as MCR-ALS, ReactLab or others. The Matlab program code is freely accessible and includes not only the proposed ambiguity analysis but also an MCR hard-modeling approach. Application studies are presented for two experimental data sets, namely for UV/Vis spectra on the relaxation of a photoexcited state of benzophenone and for Raman spectra on an aldehyde formation process.

Quality assessment of gasoline using comprehensive two-dimensional gas chromatography combined with unfolded partial least squares: A reliable approach for the detection of gasoline adulteration.

Comprehensive two-dimensional gas chromatography and flame ionization detection combined with unfolded-partial least squares is proposed as a simple, fast and reliable method to assess the quality of gasoline and to detect its potential adulterants. The data for the calibration set are first baseline corrected using a two-dimensional asymmetric least squares algorithm. The number of significant partial least squares components to build the model is determined using the minimum value of root-mean square error of leave-one out cross validation, which was 4. In this regard, blends of gasoline with kerosene, white spirit and paint thinner as frequently used adulterants are used to make calibration samples. Appropriate statistical parameters of regression coefficient of 0.996-0.998, root-mean square error of prediction of 0.005-0.010 and relative error of prediction of 1.54-3.82% for the calibration set show the reliability of the developed method. In addition, the developed method is externally validated with three samples in validation set (with a relative error of prediction below 10.0%). Finally, to test the applicability of the proposed strategy for the analysis of real samples, five real gasoline samples collected from gas stations are used for this purpose and the gasoline proportions were in range of 70-85%. Also, the relative standard deviations were below 8.5% for different samples in the prediction set.

N-way partial least squares with variable importance in projection combined to GC × GC-TOFMS as a reliable tool for toxicity identification of fresh and weathered crude oils.

In this study, N-way partial least squares (NPLS) is proposed to correlate comprehensive two-dimensional gas chromatography-time of flight mass spectrometry (GC × GC-TOFMS) data of different aromatic oil fractions (fresh and weathered) to their toxicity values. Before NPLS modeling, since drift and wander of baseline interfere with information of sought analytes in GC × GC-TOFMS data, a novel method called two-dimensional asymmetric least squares is thus developed for comprehensive correction of the baseline contributions in both chromatographic dimensions. The algorithm is termed comprehensive because it functions to correct the entire chromatogram in both dimensions and it preserves the separation information in both dimensions. In this method, a smoother is combined with asymmetric weighting of deviations from the (smooth) trend to get an effective baseline estimator in both chromatographic dimensions. After baseline correction, the NPLS model was calibrated with 20 oil fractions and evaluated by leave-one-out cross-validation. The number of latent variables was chosen on the basis of minimum root mean squares error of cross validation (RMSECV), and it was 7 (RMSECV = 0.073). The developed NPLS model was able to accurately predict the toxicity effects in the five oil fractions as prediction sets which were independent of 20 oil fractions in calibration set (RMSEP = 0.0099 and REP = 11.38 %). Finally, the newly developed n-way variable importance in projection (NVIP) was used for identification of the most influential chemical components on the toxicity values of different oil fractions. According to the high NVIP values in both chromatographic dimensions and their corresponding mass spectra, alkyl substituted three- and four-ring aromatic hydrocarbons were identified. It is concluded that multivariate chemometric methods (e.g., NPLS) combined to non-target analysis using GC × GC-TOFMS is a viable strategy to be used for analytical identification in fuel oil studies, with a potential to reduce the number of fractionation steps needed to obtain necessary chromatographic and mass spectral information.

Sulpiride-induced hyperprolactinemia in mature female rats: evidence for alterations in the reproductive system, pituitary and ovarian hormones.

BACKGROUND: The prevalence of hyperprolactinemia following administration of conven- tional antipsychotic drugs requires further investigation. The current study is designed to evaluate the effect of sulpiride (SPD)-induced hyperprolactinemia on alterations to ovarian follicular growth, gonadotropins, and ovarian hormones and to analyze the extent of potential problems in mammary glands. MATERIALS AND METHODS: A total of 40 albino Wistar rats were divided into four groups: control (no treatment), control-sham (0.3 ml olive oil), low dose SPD (20 mg/kg) and high dose SPD (40 mg/kg). All compounds were intraperitoneally (IP) administered for a period of 28 days. RESULTS: After 28 days, we dissected the rats' ovarian tissues, uterine horns and mammary glands which were sent for histological analyses. We counted the numbers of normal, atretic follicles and corpora lutea (CL). Serum levels of prolactin (PRL), estradiol, progesterone, follicle stimulating hormone (FSH) and luteinizing hormone (LH) were evaluated. SPD-administered animals showed sporadic follicular atresia in different sizes associated with higher numbers of CL on the ovaries. The mammary glands exhibited features of galactorrhea. There was remarkable (p<0.05) elevation in SPD-administered animals' uterine horn endometrium, myometrium and perimetrium thicknesses. The serum levels of PRL and progesterone significantly (p<0.05) increased, while the serum concentration of estradiol, LH and FSH notably (p<0.05) decreased according to the SPD administered dose. No histological and biological changes occurred in control-sham animals. SPD-induced animals had unsuccessful attempts at mating and decreased pregnancy rates. CONCLUSION: The present findings suggest that SPD-induced disturbances depend on PRL level. In addition, an increased PRL level is largely dependent on the administered doses of SPD.