Near infrared reflectance spectroscopy as a tool to predict non-starch polysaccharide composition and starch digestibility profiles in common monogastric cereal feed ingredients

Using in feed xylanase or stimbiotic to reduce the variability in corn nutritive value for broiler chickens

This study investigated the effects of xylanase and stimbiotic (fiber fermentation enhancer) on the response of broiler chickens fed different corn varieties and determine correlations between variables of interest. Four corn genetic varieties were selected based on their range in nutrient composition. Diets containing 600 g/kg of each corn were supplemented with 0 or 100 g/ton of xylanase or stimbiotic. A total of 1,152 one-day-old male broiler chicks (Ross 308) were divided into 12 treatments, each with 8 pens and 12 birds per pen, for a 21-day study. On d 21, performance parameters were measured, and the ileal energy and organic matter (OM) digestibility and cecal xylanase activity determined. Stimbiotic supplementation improved mFCR compared with all other treatments. There was a treatment by corn variety interaction for body weight (BW), BW gain and coefficient of variation (CV) of BW (P ≤ 0.05). Birds fed corn Variety 1 (highest neutral dietary fiber, protein and soluble arabinoxylan content) supplemented with stimbiotic had the highest BW, while Variety 2 control diet had the lowest. The BW CV in corn Variety 2 was the highest, which improved with stimbiotic supplementation. The BW CV in corn Variety 1 responded better to stimbiotic than xylanase. There were no treatment differences on BW CV in corn Varieties 3 and 4. The lowest OM digestibility was observed in birds fed corn Variety 1 with xylanase, and the highest value was associated with corn Variety 3 with xylanase (highest total arabinoxylan). Xylanase and stimbiotic supplementation increased the endogenous xylanase activity regardless of the corn variety (P ≤ 0.05). Positive correlations between corn fiber contents and phytic acid and the arabinose:xylose ratio were seen, while nonstarch polysaccharide content was negatively correlated with apparent metabolizable energy. In conclusion, corn variety influenced nutrient digestibility and broiler chicken growth. The response to supplementation with xylanase or stimbiotic varied based on the nutritional profile of corn with regards to improvements in digestibility and performance in broiler chickens.

Dietary fibre effects and the interplay with exogenous carbohydrases in poultry nutrition

A comprehensive understanding of the role of dietary fibre in non-ruminant animal production is elusive. Equivocal and conflated definitions of fibre coupled with significant analytical complexity, interact with poorly defined host and microbiome relationships. Dietary fibre is known to influence gut development, feed intake and passage rate, nutrient absorption, microbiome taxonomy and function, gut pH, endogenous nutrient loss, environmental sustainability, animal welfare and more. Whilst significant gaps persist in our understanding of fibre in non-ruminant animal production, there is substantial interest in optimizing the fibre fraction of feed to induce high value phenotypes such as improved welfare, live performance and to reduce the environmental footprint of animal production systems. In order to achieve these aspirational goals, it is important to tackle dietary fibre with the same level of scrutiny as is currently done for other critical nutrient classes such as protein, minerals and vitamins. The chemical, mechanical and nutritional role of fibre must be explored at the level of monomeric sugars, oligosaccharides and polysaccharides of varying molecular weight and decoration, and this must be in parallel to standardisation of analytical tools and definitions for speciation. To further complicate subject, exogenous carbohydrases recognise dietary fibre as a focal substrate and have varying capacity to generate lower molecular weight carbohydrates that interact differentially with the host and the enteric microbiome. This short review article will explore the interactive space between dietary fibre and exogenous carbohydrases and will include their nutritional and health effects with emphasis on functional development of the gut, microbiome modulation and host metabolism.

Cookie composition analysis by Fourier transform near infrared spectroscopy coupled to chemometric analysis

The consumption ofcookies is ever growing and during the COVID-19 pandemic reached record consumption values and it is imperative to guarantee the quality and safety of the products.Fourier transform near infrared (FT-NIR) spectroscopy, combined with chemometric techniques, provides a promising solution in that regard, due to its speed and simple sample preparation. The objective of this study was to investigate the possibilities of using FT-NIR to predict lipids, carbohydrates, fibers, proteins, salt and energy contents, as well as to identify cookies type and main cereals present in a batch of 120 commercially acquired samples. The prediction models were performed using ordinary least squares (OLS), partial least squares (PLS), and PLS based classification models including discriminant analysis (PLS-DA), k-nearest neighbors (PLS-kNN) and naïve Bayes (PLS-NB). The best prediction models allowed for good accuracies, with correlation coefficients higher than 0.9 for all studied nutritional parameters. PLS-kNN methodology was able to identify all 5 main cereals (wheat, integral wheat, oat, corn and rice) as well as the 14 types of cookies based on the nutritional contents. The developed methods were able to accurately identify the cookies type and composition, confirming the proposed methodology as a fast, reliable, environmentally friendly and non-destructive alternative to standard analytical methods.

A Rapid and Accurate Quantitative Analysis of Cellulose in the Rice Bran Layer Based on Near-Infrared Spectroscopy

Cultivating rice varieties with lower cellulose content in the bran layer has the potential to enhance both the nutritional value and texture of brown rice. This study aims to establish a rapid and accurate method to quantify cellulose content in the bran layer utilizing near-infrared spectroscopy (NIRS), thereby providing a technical foundation for the selection, screening, and breeding of rice germplasm cultivars characterized by a low cellulose content in the bran layer. To ensure the accuracy of the NIR spectroscopic analysis, the potassium dichromate oxidation (PDO) method was improved and then used as a reference method. Using 141 samples of rice bran layer (rice bran without germ), near-infrared diffuse reflectance (NIRdr) spectra, near-infrared diffuse transmittance (NIRdt) spectra, and fusion spectra of NIRdr and NIRdt were used to establish cellulose quantitative analysis models, followed by a comparative evaluation of these models' predictive performance. Results indicate that the optimized PDO method demonstrates superior precision compared to the original PDO method. Upon examining the established models, their predictive capabilities were ranked in the following order: the fusion model outperforms the NIRdt model, which in turn surpasses the NIRdr model. Of all the fusion models developed, the model exhibiting the highest predictive accuracy utilized fusion spectra (NIRdr-NIRdt (1st der)) derived from preprocessed (first derivative) diffuse reflectance and transmittance spectra. This model achieved an external predictive R2p of 0.903 and an RMSEP of 0.213%. Using this specific model, the rice mutant O2 was successfully identified, which displayed a cellulose content in the bran layer of 3.28%, representing a 0.86% decrease compared to the wild type (W7). The utilization of NIRS enables quantitative analysis of the cellulose content within the rice bran layer, thereby providing essential technical support for the selection of rice varieties characterized by lower cellulose content in the bran layer.

An approach to detecting diphenylamine content and assessing chemical stability of single-base propellants by near-infrared reflectance spectroscopy

Diphenylamine (DPA) as a stabilizer component plays an important role in maintaining the chemical stability of single-base propellants (SBPs). This work investigated the feasibility of rapidly detecting the content of DPA in SBP by near-infrared reflectance spectroscopy (NIRS). The quantitative NIR model was developed by intervals selection, spectral pretreatment and factor number optimization. The optimal spectral intervals were determined to be 1081 nm ∼ 1280 nm and 1378 nm ∼ 1602 nm based on the characteristic spectral peaks of DPA. By comparing the performance of the developed models with different preprocessing methods, the best preprocessing method was standard normal variate transformation (SNV) + de-trending (Dr) + Smoothing. The optimal number of factors was 6 for DPA model. Partial least squares (PLS) regression was used to establish the calibration models of DPA. For the developed model, the determination coefficients of calibration and prediction (R_c², R_p²) were 0.9907 and 0.9884, respectively. The root mean square errors of calibration and prediction (RMSEC, RMSEP) were 0.0310 and 0.0342, respectively. The samples in the prediction set were predicted by the developed model, and the average absolute error of the proposed and reference method was only 0.0265. The developed model can be applied in rapid monitor the content of DPA in SBP. In addition, vieille test have demonstrated that the chemical stability of SBP became worse with the decrease of DPA content. The content of DPA contained in the SBP with qualified chemical stability is not less than 0.8753%. Thus, the developed model can be used to judge whether the chemical stability of SBP is qualified or unqualified.

Evaluation of Feed Near-Infrared Reflectance Spectra as Predictors of Methane Emissions from Ruminants

Feed chemical composition is associated with methane (CH4) formation in the rumen, and thus CH4 yields (Ym; CH4 emitted from per unit of dry matter intake) could be predicted using near-infrared reflectance spectroscopy (NIRS) of feeds fed to ruminants. Two databases of NIRS data were compiled from feeds used in experiments in which CH4 yields had been quantified in respiration chambers. Each record in the databases represented a batch of feed offered to a group of experimental animals and the mean CH4 yield for the group. A near-infrared reflectance spectrum was obtained from each feed, and these spectra were used to generate a predictive equation for Ym. The predictive model generated from brassica crops and pasture fed at a similar feeding level (n = 40 records) explained 53% of the variation in Ym and had a reasonably good agreement (concordance correlation coefficient of 0.77). The predictive ability of the NIRS calibration could be useful for screening purposes, particularly for predicting the potential Ym of multiple feeds or feed samples, rather than measuring Ym in animal experiments at high expenses. It is recommended that the databases for NIRS calibrations are expanded by collecting feed information from future experiments in which methane emissions are measured, using alternative algorithms and combining other techniques, such as terahertz time-domain spectroscopy.

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.