Detect molecular spectral features of newly developed Vicia faba varieties and protein metabolic characteristics in ruminant system using advanced synchrotron radiation based infrared microspectroscopy: A preliminary study

Using vibrational molecular spectroscopy to reveal carbohydrate molecular structure properties of faba bean partitions and faba bean silage before and after rumen incubation in relation to nutrient availability and supply to dairy cattle

To our knowledge, there is limited study on the relationship between the molecular structure of feed and nutrient availability in the ruminant system. The objective of this study is to use advanced vibrational molecular spectroscopy (attenuated total reflection [ATR]-Fourier transform infrared [FT/IR]) to reveal carbohydrate molecular structure properties of faba bean partitions (stem, leaf, whole pods [WP], and whole plant) and faba bean silage before and after rumen incubation in relation to nutrient availability and supply to dairy cattle. The study included the correlation between carbohydrate-related spectral profiles and chemical profiles, feed energy values, Cornell Net Carbohydrate and Protein System carbohydrate fractions, and rumen degradation parameters of faba bean samples (whole crop, stem, leaf, WP, and silage) before and after rumen incubation. FTIR spectra of faba bean sample before and after 12 and 24 h rumen incubations were collected with JASCO FT/IR-4200 with ATR at mid-IR range (ca. 4000-700 cm^-1 ) with 128 scans and at 4 cm^-1 resolution. The univariate molecular spectral analysis was carried out using OMNIC software. The results show that ATR-FT/IR spectroscopic technique could detect the change of microbial digestion to carbohydrate-related molecular structure. The spectral parameters of feed rumen incubation residues had a stronger correlation with less degradable carbohydrate fractions (neutral detergent fiber, acid detergent fiber, acid detergent lignin, hemicellulose, and cellulose) while spectral profiles of original faba samples had a stronger correlation with easily degradable carbohydrate fractions (starch). In conclusion, rumen degradation of carbohydrate contents can be reflected in the change of its molecular spectral profiles. The study shows that vibrational molecular spectroscopy (ATR-FT/IR) shows high potential as a fast analytical tool to evaluate and predict nutrient supply in the ruminant system.

Using Mid-IR spectroscopy (ATR-FTIR) as a fast analytical tool to reveal association between protein spectral profiles and metabolizable protein supply, protein rumen degradation characteristics and estimated intestinal protein digestion before and after rumen incubation of faba bean partitions and faba bean silage

To our knowledge, there is little research done in using vibrational MID-IR molecular spectroscopy- attenuated total reflectance - Fourier transform infrared spectroscopy (ATR-FTIR) for ruminant system study. The objective of this study was to use ATR-FTIR as a fast analytical tool to reveal association between protein molecular structure in faba and metabolizable protein supply and nutrient delivery, and to explore the relationship between protein molecular structure in original and ruminal degraded residue and in situ rumen protein degradation and protein metabolism characteristics of faba bean samples (whole crop, stem, leaf, whole pods, and faba silage). The experiment for ruminant nutrition research was RCBD. Fourier transform Infrared (FTIR) spectra of faba samples before and after 12 and 24 h rumen incubations were collected with JASCO FT/IR-4200 with ATR at mid-IR range (ca. 4000-700 cm^-1) with 128 scans and at 4 cm^-1 resolution. The univariate molecular spectral analysis was carried using OMNIC software. Protein related spectral parameters before and after rumen degradation included amide region (ca. 1730-1480 cm^-1), amide I region (ca. 1713-1558 cm^-1) and amide II region (ca. 1558-1485 cm^-1). Within amide I region, α-helix (ca. 1644 cm^-1) and β-sheet (ca. 1630 cm^-1) were studied. The results showed that ATR-FTIR protein molecular spectral features were significantly different before and after rumen incubation. Protein availability and digestion characteristic are mainly determined by original ATR-FTIR spectral profiles. Total truly digestible protein value (DVE) of faba partitions could be predicted with this equation: DVE (g/kg DM) = 1207.7 HAII12 + 228.7 alpha_beta24 - 310.8 (with R-square = 0.94, RSD = 8.06, model P < 0.001). The study shows that vibrational MID-IR molecular spectroscopy (ATR-FTIR) show a high potential to be a fast analytical tool to predict nutrient delivery.

Evaluation of the nutritional value of faba beans with high and low tannin content for use as feed for ruminants

BACKGROUND

Faba bean varieties with low or zero tannin content have been developed in Canada to overcome the negative effects of condensed tannins on the utilization by ruminants of crude protein (CP) and starch. However, their nutritional value has not been evaluated for incorporation in dairy rations. The objectives of this study were to investigate (i) the chemical profile; (ii) the Cornell Net Carbohydrate and Protein System (CNCPS) protein and carbohydrate subfractions; (iii) the energy values; (iv) the ruminal, intestinal, and total digestibility of CP; (v) the metabolizable protein (MP) supply to dairy cows; and (vi) the protein-inherent molecular spectral characteristics of brown-seeded (var. Fatima) faba beans with high tannin content and yellow-seeded (var. Snowbird) faba beans with low tannin content.

RESULTS

The results revealed that Fatima beans had higher (P < 0.001) CP content than Snowbird (324 versus 295 g kg^-1 dry matter (DM)), and lower (P < 0.01) starch content than Snowbird (411 g kg^-1 DM versus 444 g kg^-1 DM). Fatima had a lower (P = 0.001) soluble subfraction (201 g kg^-1 DM versus 220 g kg^-1 DM) and higher (P < 0.05) slowly degradable fiber-bounded (24.9 g kg^-1 DM versus 14.7 g kg^-1 DM) and non-degradable (3.24 g kg^-1 DM versus 0 g kg^-1 DM) CNCPS CP subfractions than Snowbird. Fatima had higher (P = 0.03) MP content (117 g kg^-1 DM versus 111 g kg^-1 DM) and metabolizable energy content (ME) 3.12 Mcal kg^-1 versus 3.10 Mcal kg^-1 ) than Snowbird. Molecular spectral intensities of amide I and II proteins (height and area) of Fatima were higher (P < 0.05) than those of Snowbird, reflecting their higher CP content. The ratio of protein spectral intensities, the amide I : amide II height ratio, and the α-helix : β-sheet height ratio differed (P < 0.05) between the two types of bean, highlighting differences in their inherent protein molecular structures.

CONCLUSION

The (Fatima) faba beans with high condensed tannin content had higher MP and ME content. On average, both Faba beans had higher ME and MP content than barley grains, highlighting their promising nutritional value for dairy rations. © 2021 Society of Chemical Industry.

Advances in infrared spectroscopy and hyperspectral imaging combined with artificial intelligence for the detection of cereals quality

Cereals provide humans with essential nutrients, and its quality assessment has attracted widespread attention. Infrared (IR) spectroscopy (IRS) and hyperspectral imaging (HSI), as powerful nondestructive testing technologies, are widely used in the quality monitoring of food and agricultural products. Artificial intelligence (AI) plays a crucial role in data mining, especially in recent years, a new generation of AI represented by deep learning (DL) has made breakthroughs in analyzing spectral data of food and agricultural products. The combination of IRS/HSI and AI further promotes the development of quality evaluation of cereals. This paper comprehensively reviews the advances of IRS and HSI combined with AI in the detection of cereals quality. The aim is to present a complete review topic as it touches the background knowledge, instrumentation, spectral data processing (including preprocessing, feature extraction and modeling), spectral interpretation, etc. To suit this goal, principles of IRS and HSI, as well as basic concepts related to AI are first introduced, followed by a critical evaluation of representative reports integrating IRS and HSI with AI. Finally, the advantages, challenges and future trends of IRS and HSI combined with AI are further discussed, so as to provide constructive suggestions and guidance for researchers.