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Amino acid profile of the quinoa (Chenopodium quinoa Willd.) using near infrared spectroscopy and chemometric techniques
Abstract
The high content of amino acids of the quinoa, especially essential amino acids (higher than other cereals) makes a food increasingly demanded by consumers. A total of twelve amino acids (arginine, cystine, isoleucine, leucine, lysine, phenylalanine, proline, serine, threonine, tryptophan, tyrosine and valine) were analyzed in quinoa samples from Chile by near infrared spectroscopy (NIR) with direct application to the samples of a remote fibre-optic reflectance probe. The calibration results using modified partial least squares (MPLS) regression satisfactorily allowed the determination of the concentrations of this amino acid group with high multiple correlation coefficients (RSQ=0.97-0.71) and low standard prediction errors (SEPC=0.07-0.20). The prediction capacity (RPD) for the arginine, the cystine, the isoleucine, the lysine, the serine, the threonine, the tryptophan, the tyrosine and the valine ranged between 2.6 and 5.2, for the rest of amino acids were higher to 1.8, indicating that the NIRS equations obtained were applicable to unknown samples. It has confirmed that NIRS technology is a method that may be useful to replace the traditional methods for routine analysis of some amino acids.
... Quinoa, considered a potential future major crop, has attracted intense public attention since the Food and Agriculture Organization of the United Nations announced the "International Year of Quinoa" in 2013 [3,4]. Previous analyses showed that quinoa constitutes all of the essential amino acids required for human health, and contains more protein content than rice, barley, corn, rye and sorghum [5][6][7][8][9][10][11]. Moreover, quinoa can sustainably endure well in extreme growth conditions, including drought, salinity and frost [12,13]. ...
Quinoa (C. quinoa) is considered a gluten-free food with abundant nutrients and high tolerance to multiple abiotic stresses, which is the potential to become a major crop in future. Genetic manipulation will provide powerful tools to investigate the function and mechanism of those important genes in the regulation of quinoa development and stress responses, and further improve the quinoa in the field. However, the efficient plant transformation system for quinoa has not been well developed yet. Here, we established two rapid and efficient transformation systems for quinoa by using hairy roots and agroinfiltration of leaves, which provide useful tools for quick analysis of gene function. Hairy roots were obtained from three types of explants: cotyledon-nod with hypocotyl, cotyledon itself, and hypocotyl pieces. Interestingly, explants of cotyledon-nod with hypocotyl showed the highest transformation efficiency at 67.9%, and cotyledon displayed medium efficiency at 42.2%, while hypocotyl explants with the lowest at 31.6%. We also obtained transgenic quinoa roots successfully in-vivo, which showed low efficiency but provides a potential method to test gene functions in live plants. By using young leaves for agroinfiltration, direct injection showed a better transgenic effect compared with vacuum penetration. In juxtaposition, the transformation systems using both hairy root and leaf infiltration establish an efficient and convenient way to manipulate and analyze gene functions in quinoa, and a potential strategy for transgenic quinoa.
... NIRS is widely accepted as a methodology to predict amino acid composition in cereals and protein-rich feedstuffs, as an alternative to wet chemistry methods (Chen et al., 2013). Many models exist for different types of samples: sunflower meal (Fontaine et al., 2001), peas (Fontaine et al., 2001), cereal ingredients (Fontaine et al., 2002;Hoehler et al., 2005), millet (Chen et al., 2013), dried distillers grains with solubles (DDGS) (Zhou et al., 2012), processed animal proteins or meals (Dale et al., 2012;Fontaine et al., 2001;Hoehler et al., 2005), rice (Fontaine et al., 2002;Zhang et al., 2011;Wu et al., 2002), rapeseed (Chen et al., 2011;Fontaine et al., 2001), quinoa (Escuredo et al., 2014), peanuts (Wang et al., 2013) and soya bean (Fontaine et al., 2001;Pazdernik et al., 1997). The developed models were all developed on individual feedstuffs and showed that RSQ VAL was dependent on the variability in the data set, but there were also differences in the models developed depending on the feed matrix. ...
Pig farming systems face an increasingly diversified challenge to consider simultaneously the economic, environmental, and social pillars of sustain ability. For animal nutrition, this requires the development of smart feeding strategies able to integrate these different dimensions in a dynamic way and to be adapted as much as possible to each individual animal. These developments can be supported by digital technologies including data collection and processing, decision making and automation of applications. Classical traits such as feed intake and growth benefit from new technologies that can be measured more frequently. New sensors can be indicative for other traits related to body composition, physiological status, activity, feed efficiency, or rearing environment. A challenge for data collection is to obtain information on a large number of animals and with sufficient frequency, quality, and precision and use it cost-effectively. Another challenge is to analyse the ever-increasing volume of data and use it in decision-making. Nutritional models for pigs and sows, classically mechanistic, have to evolve to integrate real-time data. With the development of data-driven modelling methods (e.g., machine-learning or deep-learning), a synergy between mechanistic models and data-driven approaches is required in smart pig nutrition. Moreover, the practical application of smart pig nutrition must consider the evolution in pig farming systems towards increased diversity in terms of size, space allowance, and outdoor access, and return on investment. Finally, the transition of pig nutrition in the digital era must consider the social acceptance of an increasing role of digital technologies in animal production systems.KeywordsActivityArtificial intelligenceAutomatonConcept-driven modellingData collectionData-driven modellingData processingDecision support systemFattening pigsFeed efficiencyFeed intakeGestating sowHealth statusLactating sowMineralNutritionNutritional requirementsPerformancePhysiological statusPig farming systemPrecision feedingRearing environmentSensors
... NIRS is widely accepted as a methodology to predict amino acid composition in cereals and protein-rich feedstuffs, as an alternative to wet chemistry methods (Chen et al., 2013). Many models exist for different types of samples: sunflower meal (Fontaine et al., 2001), peas (Fontaine et al., 2001), cereal ingredients (Fontaine et al., 2002;Hoehler et al., 2005), millet (Chen et al., 2013), dried distillers grains with solubles (DDGS) (Zhou et al., 2012), processed animal proteins or meals (Dale et al., 2012;Fontaine et al., 2001;Hoehler et al., 2005), rice (Fontaine et al., 2002;Zhang et al., 2011;Wu et al., 2002), rapeseed (Chen et al., 2011;Fontaine et al., 2001), quinoa (Escuredo et al., 2014), peanuts (Wang et al., 2013) and soya bean (Fontaine et al., 2001;Pazdernik et al., 1997). The developed models were all developed on individual feedstuffs and showed that RSQ VAL was dependent on the variability in the data set, but there were also differences in the models developed depending on the feed matrix. ...
Feed accounts for the largest cost item in livestock production. Optimising the feed to the animal needs is therefore pivotal to efficient animal production and to minimise environmental and climate impacts. Classically, feed has been optimised based on table values and with the possibility for adjustment due to differences in chemical composition of ingredients. The latter requires tedious and costly wet chemical methods and has further the limitation that it cannot be used for the measurement of the nutritive value in real time. Near-infrared reflectant spectroscopy (NIRS), which utilises the interaction between light and matter, holds the potential to be used as online tool for measurements not only of nutrient composition, but also on nutritional value, provided that sufficiently large reference databases are available. This chapter discusses the recent progress in the development of calibration equations for the measurements of the digestibility of nutrients and energy values based on NIR scans of feedstuffs and diets and faecal residues, and how NIRS can be used to control the quality of feeds from a feed mill in real time and optimise the provision of nutrients for animals during growth and production. The use of NIRS calibrations developed based on faecal residues as a tool to select pigs with improved nutrient digestibility and value is also described and discussed. Real-time quality control of feeds provided to the animal has a central role in the implementation of smart nutrition in livestock systems.KeywordsAcid detergent fibre (ADF)Amino acidsApparent ileal digestibilityCalibrationCross-validationDigestible energy (DE)Gross energy (GE)Crude proteinFaecal NIRSLysineMetabolisable energy (ME)Near-infrared reflectant spectroscopy (NIRS)Net energy (NE)Neutral detergent fibre (NDF)Nitrogen free extract (NFE)Nutritive valueStarchSugar + starchTotal tract digestibilityValidation
... This reaction causes up to 65% degradation of several amino acids [77]. In addition, amino acids containing sulfur, such as methionine, are more sensitive and degrade quickly in HCl during the sample processing [70,78]. Therefore, no predictive models could be constructed for the missing amino acids using NIRS. ...
Insects are a sustainable protein source for food and feed. The yellow mealworm (Tenebrio molitor L.) is a promising candidate for industrial insect rearing and was the focus of this study. This research revealed the diversity of Tenebrio molitor larvae in the varying larval instars in terms of the nutritional content. We hypothesized that water and protein are highest in the earlier instar, while fat content is very low but increases with larval development. Consequently, an earlier instar would be a good choice for harvest, since proteins and amino acids content decrease with larval development. Near-infrared reflectance spectroscopy (NIRS) was represented in this research as a tool for predicting the amino and fatty acid composition of mealworm larvae. Samples were scanned with a near-infrared spectrometer using wavelengths from 1100 to 2100 nm. The calibration for the prediction was developed with modified partial least squares (PLS) as the regression method. The coefficient for determining calibration (R2C) and prediction (R2P) were >0.82 and >0.86, with RPD values of >2.20 for 10 amino acids, resulting in a high prediction accuracy. The PLS models for glutamic acid, leucine, lysine and valine have to be improved. The prediction of six fatty acids was also possible with the coefficient of the determination of calibration (R2C) and prediction (R2P) > 0.77 and >0.66 with RPD values > 1.73. Only the prediction accuracy of palmitic acid was very weak, which was probably due to the narrow variation range. NIRS could help insect producers to analyze the nutritional composition of Tenebrio molitor larvae fast and easily in order to improve the larval feeding and composition for industrial mass rearing.
... Amount of honey reducing sugars depend on time of storage and collection [31,32]. The content of reducing sugar in honey is affected by botanical origin (types of flowers as the source of nectar), geographical origin (for beekeeping or meliponiculture), climate, processing and storage [33,34,35]. It is composed mainly fructose (~38% w/v) which is responsible for the sweetness; and glucose (~31%) which depends upon the nectar source, and sucrose (~1%) in lesser amount. ...
The research was carried out to determine the physico-chemical quality aspects of honey harvested from northeastern Ethiopia. Twenty four honey samples were collected from four locations and two hive types. R software was used to analyze the data. The average values were 14.47%, 0.28%, 28.22 meq/kg, 4.28, 48.48 mg/kg, 13.75 Goth scale, 2.56%, and 52.43% for moisture, ash, acidity, pH value, hydroxyl-methyl-furfural (HMF), diastase, sucrose, and reducing sugars, respectively. The honey of highlands (17.43%) was higher (P
... Fourier Transform Near-infrared spectroscopy (FT-NIR) is a versatile analytical tool that is capable of non-invasive measurement of analytes. It has been extensively used in the biotech and food industry for the measurement of amino acids [12][13][14][15][16][17][18]. Some key advantages that NIR offers include negligible sample preparation requirement, processing of complex multivariate data, lower detection time, real time multi-parameter monitoring, and reduced manpower [19,20]. ...
The biopharmaceutical industry extensively employs Chinese hamster ovary (CHO) cell culture for monoclonal antibody production. Amino acids represent an essential source of nutrients in all CHO cell culture media, and their concentration is known to significantly impact cell viability, titre, and monoclonal antibody critical quality attributes. In this study, a robust Fourier transform near-infrared spectroscopy (FT-NIR) based quantification method has been developed for of all 20 amino acids (0-24 mM), as well as concentrations of glucose (0-6.7 mg mL-1), lactate (0-2.7 mg mL-1), and trastuzumab (0-2.5 mg mL-1) in the CHO cell culture. Near infra-red absorbance spectrum in the range of 4000-11,000 cm-1 were acquired, and spectra pre-processing through smoothening and derivatives were employed to enhance key characteristic signals. High-performance liquid chromatography with pre-column derivatization was used as the orthogonal analytical tool for quantification. Principal component analysis and partial least squares regression were employed for region selection and calibration model development, respectively. The results demonstrate that a good calibration statistic with the acceptable coefficient of determinations for both calibration (Rc2 = 0.94-0.99) and prediction (Rp2 = 0.83-0.98) could be achieved, along with high RPD values (>3) for all components except alanine (2.4). The external validation study also exhibited a satisfactory outcome (REV2 = 0.89-0.99, RMSE = 0.04-1.04), validating the model's ability to predict the concentrations of the respective species. The calibration models were successfully applied for at-line monitoring of two perfusion runs on a 10 L scale. To our knowledge, this is the first application where NIR spectroscopy-based measurement of all 20 amino acids in mammalian cell culture samples has been demonstrated. The proposed tool can play a critical role as biopharma manufacturers implement continuous processing as well as for facilitating process analytical technology-based control of mammalian cell culture processes.
... However, the difference in the protein content was not significant among the studied quinoa cultivars (Table 6). In terms of the protein content of such essential amino acids, such as valine, lysine, and threonine, the protein in quinoa grains was close to the reference protein indexes and comparable with the amino acid content of other research [31,46]. ...
The article presents the research findings from the analysis of the growth, development, and yield formation characteristics as well as grain amino acid composition of quinoa (Chenopodium quinoa Willd.). The aim of this research was to assess the adaptability of quinoa, a new alternative crop for the Non-Chernozem conditions of Moscow’s urban region. Five quinoa cultivars were tested, namely Brighest Brillian, Red Faro, Cherry Vanilla, Titicaca, and Regalona and were grown on sod-podzolic soil with wide-row hill-drop planting. For four years, the quinoa cultivars produced high yields without fertilizer and pesticide application—on average, 2.08–2.59 tons of grain per hectare—with a high content of protein and essential amino acids, primarily valine, lysine, and threonine. The Cherry Vanilla and Regalona cultivars had the highest grain yield on average (2.59 and 2.39 t/ha, respectively). Being able to produce crops in years with different temperatures and moisture supply, they were described as cultivars with high flexibility. However, none of the studied cultivars provided a sustained yield. The total protein content in the quinoa grains grown in 2020 ranged from 12.50 to 13.96% with high essential amino acids scores, such as valine, lysine, and threonine. The cultivar Red Faro was characterized by the highest ecological plasticity, stability, and resistance to the environmental conditions of Moscow’s urban region.
Quinoa is considered a "full nutritional food" owing to its high nutrition value. However, the whole grain form of quinoa is not always convenient direct consumption in everyday life. Extrusion is effective viable solution to this issue. In this study, the metabolic characterization of white quinoa (WQ) and extruded white quinoa (EWQ) was performed by investigating the characteristic amino acids, fatty acids, organic acids, and phenolic content of WQ after extrusion. A total of 24 amino acids, 25 organic acids, 32 fatty acids and 50 phenolics were identified in both WQ and EWQ, The results showed that extrusion and extrusion temperature significantly (p < 0.05) affect the content of amino acids, fatty acids, organic acids and phenolics in WQ. For example, the content of glutaconic acid (1231.9 μg/g of EWQ-180 to 1926.4 μg/g of WQ) and L-aspartic acid (115.47 μg/g of EWQ-140 to 643.70 μg/g of WQ) in WQ was significantly decreased, while L-serine (138.01 μg/g of WQ to 201.04 μg/g of EWQ-160) was increased respectively after extrusion. Among the EWQ samples, the highest content of glutaconic acid (1447.9 μg/g), L-aspartic acid (270.32 μg/g), and L-serine (201.04 μg/g) was observed in EWQ-160, EWQ-180 and EWQ-160, respectively. These results indicated extrusion affects the content of various amino acids differently and that the changes are dependent on the extrusion temperature. Similar result was also observed for fatty acids, organic acids, and phenolics. In conclusion, extrusion has potential in the processing of quinoas and the metabolic characterization of amino acids, fatty acids, organic acids, and phenolics in grains could be effectively analyzed using metabolomics.
In this study, 279 samples of brown rice grains and their flour, selected from a larger original population, were scanned by NIRSystem model 5000 mono-chromator in these two kinds of sample status for near-infrared reflectance spectroscopy (NIRS) analysis. Spectral pretreatment method 2,8,8,1 combined with SNV+D scatter correction was found suitable for developing calibration equations for amino acids. Equations for total amino acid content and for all individual amino acids, excluding cystine, methionine and tyrosine, were developed with this spectral pretreatment method. These equations had low SECV (0.010–0.063%) and SEP (0.011–0.066%); with high 1−VR (0.878–0.960), R2 (0.837–0.947) and SD/SEP (2.421–4.333). The results suggest that equations for the thirteen amino acids from the two sample categories can be directly used to estimate the amino acid composition in brown rice. This indicates once more that NIRS is a powerful technology that could be very useful for the determination of amino acid content in breeding programs that involve brown rice as well as for quality control in the food industry.
Prolamin content of buckwheat flour and processed foods was 24.2-42.1 mg/kg dry material measured by ELISA. According to in vitro results buckwheat is suitable for use in coeliac diet, although it contains some antinutritive materials, protease inhibitors and tannin. The allergenic properties of buckwheat are poorly understood. In our investigation intensity of the 24 kD protein band of buckwheat, of which allergenic activity is known has decreased, and 30-35 kD protein associations have been formed after heat treatment. Immunochemical reaction of buckwheat proteins were studied with blood specimens of coeliac and healthy persons.
Particle size, scatter, and multi-collinearity are long-standing problems encountered in diffuse reflectance spectrometry. Multiplicative combinations of these effects are the major factor inhibiting the interpretation of near-infrared diffuse reflectance spectra. Sample particle size
accounts for the majority of the variance, while variance due to chemical composition is small. Procedures are presented whereby physical and chemical variance can be separated. Mathematical transformations—standard normal variate (SNV) and de-trending (DT)—applicable to individual
NIR diffuse reflectance spectra are presented. The standard normal variate approach effectively removes the multiplicative interferences of scatter and particle size. De-trending accounts for the variation in baseline shift and curvilinearity, generally found in the reflectance spectra of
powdered or densely packed samples, with the use of a second-degree polynomial regression. NIR diffuse NIR diffuse reflectance spectra transposed by these methods are free from multi-collinearity and are not confused by the complexity of shape encountered with the use of derivative spectroscopy.
Quinoa (Chenopodium quinoaWilld.) has been cultivated in the Andean region for thousands of years, providing highly nutritious food to poor farmers in the Andes. The conditions for crop growth are very difficult in the high region of the Andes, where the most harmful abiotic adverse factors that affect crop production are drought, frost, soil salinity, hail, snow, wind, flooding, and heat.Quinoa can grow with only 200 mm of rainfall in pure sand. Fourteen lines with improved drought resistance have been identified, and several drought-mediating mechanisms have been found. The crop has also demonstrated unusually high salt tolerance; many varieties can grow in salt concentrations as high as those found in seawater (40 mS cm), and four lines have been identified with even higher tolerance. Quinoa also has a high degree of frost resistance, surviving −8°C for up to 4 hours, depending on phenological phase and variety.
Scale differences of individual near-infrared spectra are identified when set-independent standard normal variate (SNV) and de-trend (DT) transformations are applied in either SNV followed by DT or DT then SNV order. The relationship of set-dependent multiplicative scatter correction (MSC) to SNV is also referred to. A simple correction factor is proposed to convert derived spectra from one order to the other. It is suggested that the suitable order for the study of changes using difference spectra (when removing baselines) should be DT followed by SNV, which leads to all derived spectra on the scale of mean zero and variance equal to one. If baselines are identical, then SNV scale spectra can be used to calculate differences.
Quinoa (Chenopodium quinoaWilld.) and kañiwa (Chenopodium pallidicauleAellen) are native food plants of high nutritional value grown in the Andean region and used as food by the Incas and previous cultures. Quinoa and kañiwa served as a substitute for scarce animal proteins and are still one of the principal protein sources of the region. The importance of these proteins is based on their quality, with a balanced composition of essential amino acids similar to the composition of casein, the protein of milk. According to studies at the Universidad Nacional Agraria La Molina (UNALM), quinoa and kañiwa have a very high chemical score, and one cultivar of quinoa, Amarilla de Marangani, does not have any limiting amino acid.It is also important to recognize and utilize the relatively high quantity of oil in quinoa and kañiwa. These grains can be a potential raw material for oil extraction. The highest percentage of fatty acids present in these oils is Omega 6 (linoleic acid), being 50.2% for quinoa and 42.6% for kañiwa. The fatty acid composition is similar to corn germ oil. The concentrations of γ- and α-tocoferol were for quinoa 797.2 and 721.4 ppm, and for kañiwa 788.4 and 726 ppm, respectively.Quinoa and kañiwa can been utilized in weaning food mixtures. Two dietary mixtures have been formulated: quinoa-kañiwa-beans and quinoa-kiwicha-beans, with high nutritional value. The mixtures had PER values close to that of casein: 2.36 and 2.59, respectively (casein 2.5). Also, elderly people and those with a need to lose weight can benefit from consumption of quinoa and kañiwa. The high content of dietary fiber has many positive health effects, for example, it can reduce the level of cholesterol in the blood and improve digestion. For this reason, consumers in developed countries may also have an interest in including quinoa into their diet.
The ability of NIRS to predict the true ileal digestible amino acid content of commonly used feedstuffs was evaluated. Feed samples (n=263) tested for ileal digestibility in caecectomized cocks were scanned, and calibrations were developed using the feedstuff (category) of interest, as well as other feedstuff samples which improved the quality of the calibration (`expansion' samples). The resulting calibrations were able to explain 88–95% of the variation in digestible lysine, and 80–86% of the variation in digestible methionine in meat and bone meal, fishmeal, and poultry byproducts. For soybean meal and wheat grain, calibrations were obtained which explained 55–62% of the variation in digestible lysine and 64–84% of the variation in digestible methionine, the lower explained variation in lysine in these products being linked to the smaller number of samples available to make the calibration and the relatively smaller variation encountered in digestible lysine. Comparing the variation explained by NIRS with the variation explained by nitrogen-based regression showed that nitrogen-based regression worked equally well for wheat samples; however, for soybean meal and the animal meals NIRS was more accurate: the improvements in explained variation observed ranged from 14–81% points. These data thus suggest that NIRS is a tool which can be developed for the rapid prediction of the nutritional value of feedstuffs with a precision which makes it attractive for use as a routine quality control tool in feed mills.
MiGao, prepared from rice flour and sticky rice flour, is a kind of steam cooked Chinese cake. Staling of MiGao resulted in loss in texture and eating quality. Moisture content, water activity, texture, differential scanning calorimetry thermograms and sensory quality of MiGao, were monitored and were found to be significantly affected by cake staling when stored at room temperature for up to 5 days. The moisture content decreased after 2 days of storage and during the following days the crumb moisture content remained practically unchanged. Firmness was developed mainly during the first day of storage, remained at a similar level from day 2 to 3 and increased slightly after the third day of storage. A decrease in sensory quality and acceptability of the MiGao was observed during storage. Differential scanning calorimetry was used to follow changes of starch retrogradation in MiGao crumb. Amylopectin recrystallisation in MiGao continued to increase during storage.