Evaluation of human breastmilk adulteration by combining Fourier transform infrared spectroscopy and partial least square modeling

Multivariate Curve Resolution Methodology Applied to the ATR-FTIR Data for Adulteration Assessment of Virgin Coconut Oil

Virgin coconut oil (VCO) is a functional food with important health benefits. Its economic interest encourages fraudsters to deliberately adulterate VCO with cheap and low-quality vegetable oils for financial gain, causing health and safety problems for consumers. In this context, there is an urgent need for rapid, accurate, and precise analytical techniques to detect VCO adulteration. In this study, the use of Fourier transform infrared (FTIR) spectroscopy combined with multivariate curve resolution-alternating least squares (MCR-ALS) methodology was evaluated to verify the purity or adulteration of VCO with reference to low-cost commercial oils such as sunflower (SO), maize (MO) and peanut (PO) oils. A two-step analytical procedure was developed, where an initial control chart approach was designed to assess the purity of oil samples using the MCR-ALS score values calculated on a data set of pure and adulterated oils. The pre-treatment of the spectral data by derivatization with the Savitzky-Golay algorithm allowed to obtain the classification limits able to distinguish the pure samples with 100% of correct classifications in the external validation. In the next step, three calibration models were developed using MCR-ALS with correlation constraints for analysis of adulterated coconut oil samples in order to assess the blend composition. Different data pre-treatment strategies were tested to best extract the information contained in the sample fingerprints. The best results were achieved by derivative and standard normal variate procedures obtaining RMSEP and RE% values in the ranges of 1.79-2.66 and 6.48-8.35%, respectively. The models were optimized using a genetic algorithm (GA) to select the most important variables and the final models in the external validations gave satisfactory results in quantifying adulterants, with absolute errors and RMSEP of less than 4.6% and 1.470, respectively.

Wine Lees as Source of Antioxidant Molecules: Green Extraction Procedure and Biological Activity

An ultrasound-assisted extraction method, employing ethanol and water as solvents at low temperature (30 °C) and reduced time (15 min), was proposed to extract bioactive molecules from different cultivars (Magliocco Canino, Magliocco Rosato, Gaglioppo, and Nocera Rosso) of wine lees. All the extract yields were evaluated and their contents of phenolic acids, flavonoids, and total polyphenols were determined by means of colorimetric assays and high-performance liquid chromatography coupled with diode-array detection (HPLC-DAD) and Fourier transform infrared (FTIR) techniques. Radical scavenging assays were performed and the Magliocco Canino extracted with a hydroalcoholic mixture returned the best results both against ABTS (0.451 mg mL−1) and DPPH (0.395 mg mL−1) radicals. The chemometric algorithms principal component analysis (PCA) and partial least square regression (PLS) were used to process the data obtained from all qualitative–quantitative sample determinations with the aim of highlighting data patterns and finding possible correlations between composition and antioxidant features of the different wine lees cultivars and the extraction procedures. Wine lees from Magliocco Canino and Magliocco Rosato were found to be the best vegetable matrices in terms of metabolite content and antioxidant properties. The components extracted with alcoholic or hydroalcoholic solvents, specifically (−)-epigallocatechin gallate, chlorogenic acid, and trans-caftaric acid, were found to be correlated with the antioxidant capacity of the extracts. Multivariate data processing was able to identify the compounds related to the antioxidant features. Two PLS models were optimized by using their concentration levels to predict the IC50 values of the extracts in terms of DPPH and ABTS with high values of correlation coefficient R2, 0.932 and 0.824, respectively, and a prediction error lower than 0.07. Finally, cellular (SH-SY5Y cells) antioxidant assays were performed on the best extract (the hydroalcoholic extract of Magliocco Canino cv) to confirm its biological performance against radical species. All these recorded data strongly outline the aptness of valorizing wine lees as a valuable source of antioxidants.

Comparison of the Effectiveness of Four Commercial DNA Extraction Kits on Fresh and Frozen Human Milk Samples

For-profit donor human milk organizations have DNA-based proprietary methodology for testing incoming milk for adulteration with other species’ milk. However, there is currently no standardized methodology for extracting DNA from human milk. Microbiome research has shown that DNA purity and quantity can vary depending on the extraction methodology and storage conditions. This study assessed the purity and quantity of DNA extracted from four commercially available DNA extraction kits—including one kit that was developed for human milk. This study was for method validation only. One donor provided a 90 mL human milk sample. The sample was aliquoted into 70 × 1 mL microcentrifuge tubes. Aliquots were randomized into one of three categories: fresh extraction, extraction after freezing, and extraction after purification and storage at room temperature. DNA was analyzed for purity and quantity using a NanoDrop Spectrophotometer. Results confirmed differences in DNA purity and quantity between extraction kits. The Plasma/Serum Circulating DNA Purification Mini Kit (Norgen Biotek, ON, Canada) provided significantly more DNA, and consistent purity as measured by 260/280 and 260/230 ratios. DNA quantity and purity were similar between fresh and frozen human milk samples. These results suggest that DNA purity and quantity is highest and most consistent when extracted from human milk using the Plasma/Serum Circulating DNA Purification Mini Kit amongst the kits tested in this study. Standardized methodology for extracting DNA from human milk is necessary for improvement of research in the field of human milk. To do this, future studies are recommended for optimization of DNA extraction from human milk using larger sample sizes and multiple donor parents.

Ranunculus species suppress nitric oxide production in LPS-stimulated RAW 264.7 macrophages

Aerial parts and roots from three Ranunculus species were extracted with 70% ethanol. The phytochemical composition was investigated using GC-MS and multivariate data analysis. The total phenolic and flavonoid contents were also assessed. The in vitro inhibitory properties were evaluated as nitrite concentration in LPS-stimulated RAW 264.7 macrophage cell line. All the samples induced concentration-dependent inhibitory effects, with R. sceleratus aerial parts extract being the most interesting sample (IC50 = 22.08 ± 1.32 µg/mL), even more active than the reference compound indomethacin. A very good activity was also observed for R. sardous and R. ficaria aerial parts extracts (IC50 = 51.61 ± 3.12 µg/mL and 84.55 ± 3.40 µg/mL). A lesser but noteworthy potential was also demonstrated for the root extracts. The results suggest that Ranunculus hydroalcoholic extracts are able to inhibit nitrite accumulation and may be useful in preventing inflammatory diseases mediated by excessive production of NO.

Chemometric analysis combined with FTIR spectroscopy of milk and Halloumi cheese samples according to species' origin

Food adulteration is an issue of major concern, as numerous foodstuffs and beverages do not follow their labeling. Our research interest is in the field of authenticity of dairy products and particularly cheese. Adulteration of dairy products is a well-known phenomenon, and there are numerous published studies specifically on the authenticity of cheese. In fact, substitution of a portion of fat and/or proteins, adulteration with milk of other species' origin, and mislabeling of ingredients are some of the main issues that the science of dairy products' authenticity is regularly facing. Discrimination of dairy products can be determined through several chemical or microbiological methods as presented in the literature. In addition, chemometric analysis is an important tool for interpretation of a huge load of measurements. The aim of this study is to discriminate between various milk samples, which is the primary ingredient of dairy products. Milk samples with different trademarks were analyzed. That data was combined with Halloumi cheese samples for chemometric discrimination of species' origin. The innovative point of this study is the fact that it is the first time that a research study related to dairy products includes Halloumi cheese which is a traditional Cypriot cheese, not well-studied until now. The first step of the methodology was the freeze-drying via lyophilization of the samples. Fourier transformed infrared spectroscopy (FTIR) was chosen for their chemical characterization. Moreover, interpretation of the measurements was carried out by chemometric analysis using SIMCA software. For this study, FTIR data combined with chemometrics have given a very good discrimination of the samples according to their species' origin. Chemometric methods such as PCA and OPLS-DA have been used with great success. In the future, this model will be studied regarding geographical origin of the samples.

A simple and nondestructive approach for the analysis of soluble solid content in citrus by using portable visible to near-infrared spectroscopy

A simple and nondestructive method for the analysis of soluble solid content in citrus was established using portable visible to near-infrared spectroscopy (Vis/NIRS) in reflectance mode in combination with appropriate chemometric methods. The spectra were obtained directly by the portable Vis/NIRS without destroying samples. Outlier detection was performed by using leave-one-out cross-validation (LOOCV) with the 3σ criterion, and the calibration models were established by partial least squares (PLS) algorithm. Besides, different data pretreatment methods were used to eliminate noise and background interference before calibration, to determine the one that will lead to better model accuracy. However, the correlation coefficients are all <0.62 and the results of all pretreatments are still unsatisfactory. Variable selection methods were discussed for improving the accuracy, and variable adaptive boosting partial least squares (VABPLS) method was used to get higher robustness models. The results show that standard normal variate (SNV) transformation is the best pretreatment method, while VABPLS can significantly simplify the calculation and improve the result even without pretreatment. The correlation coefficient of the best prediction models is 0.82, while the value is 0.48 for the raw data. The high performance shows the feasibility of portable Vis/NIRS technology combination with appropriate chemometric methods for the determination of citrus soluble solid content.