Predicting ruminal degradability and chemical composition of corn silage using near-infrared spectroscopy and multivariate regression.

The aim of this study was to develop and validate regression models to predict the chemical composition and ruminal degradation parameters of corn silage by near-infrared spectroscopy (NIR). Ninety-four samples were used to develop and validate the models to predict corn silage composition. A subset of 23 samples was used to develop and validate models to predict ruminal degradation parameters of corn silage. Wet chemistry methods were used to determine the composition values and ruminal degradation parameters of the corn silage samples. The dried and ground samples had their NIR spectra scanned using a poliSPECNIR 900-1700 model NIR sprectrophotometer (ITPhotonics S.r.l, Breganze, IT.). The models were developed using regression by partial least squares (PLS), and the ordered predictor selection (OPS) method was used. In general, the regression models obtained to predict the corn silage composition (P>0.05), except the model for organic matter (OM), adequately estimated the studied properties. It was not possible to develop prediction models for the potentially degradable fraction in the rumen of OM and crude protein and the degradation rate of OM. The regression models that could be obtained to predict the ruminal degradation parameters showed correlation coefficient of calibration between 0.530 and 0.985. The regression models developed to predict CS composition accurately estimated the CS composition, except the model for OM. The NIR has potential to be used by nutritionists as a rapid prediction tool for ruminal degradation parameters in the field.

Oral Squamous Cell Carcinoma Lipid Evaluation in Gingiva Tissue Stored in TRIzol via Shotgun Lipidomics and MALDI Mass Spectrometry Imaging.

Oral squamous cell carcinoma (OSCC) is the prevalent type of oral cavity cancer, requiring precise, accurate, and affordable diagnosis to identify the disease in early stages, Comprehending the differences in lipid profiles between healthy and cancerous tissues encompasses great relevance in identifying biomarker candidates and enhancing the odds of successful cancer treatment. Therefore, the present study evaluates the analytical performance of simultaneous mRNA and lipid extraction in gingiva tissue from healthy patients and patients diagnosed with OSCC preserved in TRIzol reagent. The data was analyzed by partial least-squares discriminant analysis (PLS-DA) and confirmed via matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). The lipid extraction in TRIzol solution was linear in a range from 330 to 2000 ng mL-1, r2 > 0.99, intra and interday precision and accuracy <15%, and absolute recovery values ranging from 90 to 110%. The most important lipids for tumor classification were evaluated by MALDI-MSI, revealing that the lipids responsible for distinguishing the OSCC group are more prevalent in the cancerous tissue in contrast to the healthy group. The results exhibit the possibilities to do transcriptomic and lipidomic analyses in the same sample and point out important candidates related to the presence of OSCC.

Classification of sugarcane genotypes susceptible and resistant to the initial attack of sugarcane borer Diatraea saccharalis using epicuticular wax composition.

Identifying compounds present in the sugarcane epicuticular wax and using these compounds to classify the genotypes susceptible and resistant to the initial attack of sugarcane borer (Diatraea saccharalis) was the aim of this study. A greenhouse experiment was performed in a factorial scheme with and without borer infestation using genotypes previously characterized as resistant or susceptible in field-based experiments. Sugarcane whorls of six-month-old plants were collected before (BI) and after (AI) 72 h of sugarcane borer infestation. The sugarcane epicuticular wax was extracted in both times, i.e., BI and AI and its chemical composition was assessed by gas chromatography coupled to mass spectrometry (GC-MS). Twenty-five compounds were identified for both BI and AI. Classification models were built using partial least squares for discriminant analysis (PLS-DA) and linear discriminant analysis (LDA). Variable selection methods were used to improve the classification models. Ordered predictors selection for discriminant analysis (OPSDA) selected compounds that correctly classified all the test samples before borer infestation (Error = 0.000), and exhibited the most suitable classification parameters for the test set after borer infestation (Error = 0.111). The C30 pentacyclic triterpene friedelin and a high alcohol/aldehyde ratio were associated with the classification of resistant genotypes. Our findings have applicability in developing a screening methodology for breeding programs interested in identifying genotypes resistant to the initial feeding of sugarcane borer.

Laser ablation electrospray ionization mass spectrometry imaging as a new tool for accessing patulin diffusion in mold-infected fruits.

This work describes the use of laser ablation electrospray ionization mass spectrometry imaging (LAESI imaging) to investigate the diffusion of the mycotoxin patulin from rotten to healthy areas of fruits. Slices of mold-infected and uninfected (control) apples and strawberries were prepared, and this was the only sample preparation step used. An infrared laser beam (2.94 µm) was used to irradiate the slices, resulting in the ablation of sample compounds directly ionized by electrospray and analyzed by mass spectrometry. Multivariate curve resolution - alternating least squares was applied in unfolded LAESI images to obtain relative quantity information. Patulin was not detected in the control samples but was seen in all mold-infected fruits. LAESI images showed the diffusion of patulin from the rotten area to unaffected parts of the fruits. This study points out the advantage of LAESI imaging over traditional analytical methods used to study the diffusion of mycotoxins in fruits.