A Coding Basis and Three-in-One Integrated Data Visualization Method 'Ana' for the Rapid Analysis of Multidimensional Omics Dataset.

With innovations and advancements in analytical instruments and computer technology, omics studies based on statistical analysis, such as phytochemical omics, oilomics/lipidomics, proteomics, metabolomics, and glycomics, are increasingly popular in the areas of food chemistry and nutrition science. However, a remaining hurdle is the labor-intensive data process because learning coding skills and software operations are usually time-consuming for researchers without coding backgrounds. A MATLAB® coding basis and three-in-one integrated method, 'Ana', was created for data visualizations and statistical analysis in this work. The program loaded and analyzed an omics dataset from an Excel® file with 7 samples * 22 compounds as an example, and output six figures for three types of data visualization, including a 3D heatmap, heatmap hierarchical clustering analysis, and principal component analysis (PCA), in 18 s on a personal computer (PC) with a Windows 10 system and in 20 s on a Mac with a MacOS Monterey system. The code is rapid and efficient to print out high-quality figures up to 150 or 300 dpi. The output figures provide enough contrast to differentiate the omics dataset by both color code and bar size adjustments per their higher or lower values, allowing the figures to be qualified for publication and presentation purposes. It provides a rapid analysis method that would liberate researchers from labor-intensive and time-consuming manual or coding basis data analysis. A coding example with proper code annotations and completed user guidance is provided for undergraduate and postgraduate students to learn coding basis statistical data analysis and to help them utilize such techniques for their future research.

Extraction, Purification and In Vitro Antioxidant Activity Evaluation of Phenolic Compounds in California Olive Pomace.

Olive pomace (OP) is a valuable food byproduct that contains natural phenolic compounds with health benefits related to their antioxidant activities. Few investigations have been conducted on OP from the United States while many studies on European OP have been reported. OP of Arbequina, the most common cultivar from California, was collected and extracted by water, 70% methanol and 70% ethanol, followed by purification using macroporous absorbing resin. Results showed that the extractable total phenolic content (TPC) was 36-43 mg gallic acid equivalents (GAE)/g in pitted, drum-dried defatted olive pomace (DOP), with major contributions from hydroxytyrosol, oleuropein, rutin, verbascoside, 4-hydroxyphenyl acetic acid, hydroxytyrosol-glucoside and tyrosol-glucoside. Macroporous resin purification increased TPC by 4.6 times the ethanol crude extracts of DOP, while removing 37.33% total sugar. The antioxidant activities increased 3.7 times Trolox equivalents (TrE) by DPPH and 4.7 times TrE by ferric reducing antioxidant power (FRAP) in the resin purified extracts compared to the ethanol crude extracts. This study provided a new understanding of the extraction of the bioactive compounds from OP which could lead to practical applications as natural antioxidants, preservatives and antimicrobials in clean-label foods in the US.

The application of machine-learning and Raman spectroscopy for the rapid detection of edible oils type and adulteration.

Raman spectroscopy is an emerging technique for the rapid detection of oil qualities. But the spectral analysis is time-consuming and low-throughput, which has limited the broad adoption. To address this issue, nine supervised machine learning (ML) algorithms were integrated into a Raman spectroscopy protocol for achieving the rapid analysis. Raman spectra were obtained for ten commercial edible oils from a variety of brands and the resulting spectral dataset was analyzed with supervised ML algorithms and compared against a principal component analysis (PCA) model. A ML-derived model obtained an accuracy of 96.7% in detecting oil type and an adulteration prediction of 0.984 (R2). Several ML algorithms also were superior than PCA in classifying edible oils based on fatty acid compositions by gas chromatography, with a faster readout and 100% accuracy. This study provided an exemplar for combining conventional Raman spectroscopy or gas chromatography with ML for the rapid food analysis.

Comparison of wheat, soybean, rice, and pea protein properties for effective applications in food products.

Pea and rice proteins are promising to substitute allergenic proteins, and increasingly, play important roles in the food industry because of their hypoallergenic characteristics and nutritional value. However, manufacturers generally provide limited functionality information on these proteins. Therefore, this study comprehensively compared functional properties of wheat, soybean, rice, and pea proteins for their industrial applications and illustrated correlation among various functionalities. Results showed that protein solubility (PS) was highly related to its water absorption (WA) capacity, emulsifying activity index (EAI), and emulsion stability index (ESI). The overall functionality of pea protein was close to that of soybean protein while rice protein cannot match with all other proteins. sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) analysis indicated the composition of each protein was unique. While the deconvolution of the amide I band of the Raman spectra indicated soybean and pea proteins that shared similar features, but they were different from that of wheat and rice proteins. PRACTICAL APPLICATIONS: Due to the allergenicity of wheat and soybean proteins, food manufacturers are looking for alternative protein sources. Rice and Pea proteins are promising substitutes because of their "allergen-friendly" as well as their emergence in the food market. This study provided a comprehensive comparison of the functionality of commercially available wheat, soybean, rice, and pea proteins. The information presented in this study would be helpful to food scientists, scholars, or engineers when they develop appropriate application of various proteins in food products.

¹H NMR studies of starch-water interactions during microwave heating.

The aim of the present study was to investigate the effect of microwave heating on water distribution and dynamics in starch granules during the gelatinization of starch. Starch samples treated with microwave heating, rapid conventional heating and conventional heating was measured by (1)H NMR to examine the water distribution and dynamics in rice starch granules at a water activity of 0.686. The system proton longitudinal and transverse relaxation times were determined using inversion recovery (IR) and Carr-Purcell-Meiboom-Gill (CPMG) pulse sequences. The results showed that the T1 of the water molecules in the samples treated with any of the three heating methods exhibited two distinct spectral peaks over the temperature range of 40-60 °C. With rising temperature, the long T1 component and the short T1 component approached each other, showing a trend of gradual convergence, while T2 exhibited a single peak over the entire temperature range examined. In addition, significant differences were observed in the T1 and T2 of the water molecules in the samples heated by microwave, rapid conventional and conventional. The results show that the rapid heating effect of microwave inhibits the destruction of the hydrogen bonds between starch and water molecules. In contrast, the vibration motion of polar molecules caused by microwave heating accelerates the destruction of hydrogen bonds, producing a much stronger effect than the rapid heating effect of microwave.