Vibrational spectroscopy techniques, such as Fourier-transform infrared (FTIR) and Raman spectroscopy, have been successful methods for studying the interaction of light with biological materials and facilitating novel cell biology analysis. Spectrochemical analysis is very attractive in disease screening and diagnosis, microbiological studies and forensic and environmental investigations because of its low cost, minimal sample preparation, non-destructive nature and substantially accurate results. However, there is now an urgent need for multivariate classification protocols allowing one to analyze biologically derived spectrochemical data to obtain accurate and reliable results. Multivariate classification comprises discriminant analysis and class-modeling techniques where multiple spectral variables are analyzed in conjunction to distinguish and assign unknown samples to pre-defined groups. The requirement for such protocols is demonstrated by the fact that applications of deep-learning algorithms of complex datasets are being increasingly recognized as critical for extracting important information and visualizing it in a readily interpretable form. Hereby, we have provided a tutorial for multivariate classification analysis of vibrational spectroscopy data (FTIR, Raman and near-IR) highlighting a series of critical steps, such as preprocessing, data selection, feature extraction, classification and model validation. This is an essential aspect toward the construction of a practical spectrochemical analysis model for biological analysis in real-world applications, where fast, accurate and reliable classification models are fundamental.

Aims This work aimed to evaluate the antibacterial and antifungal activities of two types of pyroligneous acid (PA) obtained from slow pyrolysis of wood of Mimosa tenuiflora and of a hybrid of Eucalyptus urophylla × Eucalyptus grandis. Methods and Results Wood wedges were carbonized on a heating rate of 1·25°C min⁻¹ until 450°C. Pyrolysis smoke was trapped and condensed to yield liquid products. Crude pyrolysis liquids were bidistilled under 5 mmHg vacuum yielding purified PA. Multi-antibiotic-resistant strains of Escherichia coli, Pseudomonas aeruginosa (ATCC 27853) and Staphylococcus aureus (ATCC 25923) had their sensitivity to PA evaluated using agar diffusion test. Two yeasts were evaluated as well, Candida albicans (ATCC 10231) and Cryptococcus neoformans. GC-MS analysis of both PAs was carried out to obtain their chemical composition. Regression analysis was performed, and models were adjusted, with diameter of inhibition halos and PA concentration (100, 50 and 20%) as parameters. Identity of regression models and equality of parameters in polynomial orthogonal equations were verified. Inhibition halos were observed in the range 15–25 mm of diameter. Conclusions All micro-organisms were inhibited by both types of PA even in the lowest concentration of 20%. Significance and Impact of the Study The feasibility of the usage of PAs produced with wood species planted in large scale in Brazil was evident and the real potential as a basis to produce natural antibacterial and antifungal agents, with real possibility to be used in veterinary and zootechnical applications.

The present study aimed to characterize the chemical composition of pyroligneous acid (PA) obtained from slow pyrolysis of the clone GG100 of Eucalyptus urophylla × Eucalyptus grandis. The efficiency of extraction of organic compounds by using different solvents—dichloromethane (DCM), diethyl ether (DE) and ethyl acetate (EA)—was evaluated. Wood discs were collected and carbonized at a heating rate of 1.25 °C/min until 450 °C. Pyrolysis gases were trapped and condensed, yielding a crude liquid product (CLP), which was refined to obtain pure PA. Then liquid–liquid extraction was carried out. Each extracted fraction was analyzed by GC-MS and the chemical compounds were identified. Experimental results showed that a larger number of chemical compounds could be extracted by using DCM and EA in comparison to diethyl ether DE. A total number of 93 compounds were identified, with phenolic compounds being the major group, followed by aldehydes and ketones, furans, pyrans and esters. Higher contents of guaiacol, phenol, cresols and furfural seem to explain the antibacterial and antifungal activity shown by PA, as reported previously in the literature. Experimental data indicated that the organic phase extracted from GG100 PA consists of a mixture of compounds similar to liquid smokes regularly used in the food industry.

The current lack of an accurate, cost-effective and non-invasive test that would allow for screening and diagnosis of gynaecological carcinomas, such as endometrial and ovarian cancer, signals the necessity for alternative approaches. The potential of spectroscopic techniques in disease investigation and diagnosis has been previously demonstrated. Here, we used attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy to analyse urine samples from women with endometrial (n = 10) and ovarian cancer (n = 10), as well as from healthy individuals (n = 10). After applying multivariate analysis and classification algorithms, biomarkers of disease were pointed out and high levels of accuracy were achieved for both endometrial (95% sensitivity, 100% specificity; accuracy: 95%) and ovarian cancer (100% sensitivity, 96.3% specificity; accuracy 100%). The efficacy of this approach, in combination with the non-invasive method for urine collection, suggest a potential diagnostic tool for endometrial and ovarian cancers.