Authentication of the Montanera Period on Carcasses of Iberian Pigs by Using Analytical Techniques and Chemometric Analyses.

The potential of two complementary analytical techniques (near infrared spectroscopy, NIRS and gas chromatography-ion mobility spectrometry, GC-IMS) was used to establish the time that Iberian pigs have been fed on acorns and pasture and to verify their genetic purity. For both techniques it was neither necessary to carry out any chemical treatment in advance nor to identify individual compounds. The results showed that both the NIR spectrum and the spectral fingerprint obtained by GC-IMS were affected by the time that the Iberian pig feeds on natural resources. High percentages of correct classification were achieved in the calibration for both techniques: >98% for the days of montanera and >96% for the breed by NIRS and >99% for the days of montanera and >98% for the breed by GC-IMS. The results obtained showed that NIR spectra taken from intact samples is a quick classification method according to the time of montanera and breed.

Evaluation of the Production Performance and the Meat Quality of Chickens Reared in Organic System. As Affected by the Inclusion of Calliphora sp. in the Diet.

The use of insects can be a possible source of protein. This study uses Calliphora sp. larvae (CLM) as a protein source in 320 one-day-old medium-growing male chicks (RedBro) during their first month of life. Chickens were randomly assigned to four dietary treatments. Each group consisted of 10 animals, and a total of 8 replicas. Control group was fed with a certified organic feed. The experimental treatments were supplemented with 5% (T2), 10% (T3), or 15% (T4) of CLM, reducing in each case the corresponding percentage of feed quantity. Productive development and meat quality were analyzed, and near infrared spectroscopy (NIRS) was used as a tool for classifying the samples. Chickens of T4 showed greater final body weight and total average daily gain, but they reduced consumption and feed conversion ratio (FCR). The chicken breast meat of T4 had lower cooking losses and higher palmitoleic acid content (p < 0.01). NIRS classified correct 92.4% of samples according to the food received. CLM is presented as a potential ingredient for the diet of medium-slow growing chickens raised in organic systems.

Near-infrared spectroscopy allows detection and species identification of Epichloë endophytes in Lolium perenne.

BACKGROUND: Perennial ryegrass (Lolium perenne) is systemically infected by seed-transmitted fungal endophytes (Epichloë sp.). The presence of Epichloë endophytes alters the nutritive quality of its hosts by modifying several plant traits. The aim of this research was to develop a fast method based on near-infrared reflectance spectroscopy (NIRS) to discriminate between perennial ryegrass plants infected (E+) or not infected (E-) with two endophyte species, Epichloë festucae var. lolii, and Epichloë typhina, using a heterogonous set of perennial ryegrass samples collected from wild grasslands and cultivars. Epichloë festucae var. lolii cultures show two morphotypes, M1 and M3, and Epichloë typhina cultures have a different M2 morphotype. RESULTS: Near-infrared reflectance spectra from E+ and E- ryegrass plants were recorded. Applying the best NIRS model for the detection of Epichloë, 93.3% of E+ plants were classified correctly. The NIRS morphotype classification was correct for 92.9% of M1 morphotype and 100% of M2 morphotypes. The NIRS classification of M3 morphotypes was not as accurate, but it was in accordance with the fungal species classification, identifying some M3 as M1 morphotypes. CONCLUSION: Near-infrared reflectance spectroscopy can detect the presence of Epichloë fungal endophytes directly in samples of perennial ryegrass, and it is adequate for discriminating among fungal species. © 2018 Society of Chemical Industry.

Qualitative and quantitative analysis of endophyte alkaloids in perennial ryegrass using near-infrared spectroscopy.

BACKGROUND: Near-infrared reflectance spectroscopy (NIRS) has been widely used in forage quality control because it is faster, cleaner and less expensive than conventional chemical procedures. In Lolium perenne (perennial ryegrass), one of the most important forage grasses, the infection by asymptomatic Epichloë fungal endophytes alters the plant nutritional quality due to the production of alkaloids. In this research, we developed a rapid method based on NIRS to detect and quantify endophyte alkaloids (peramine, lolitrem B and ergovaline) using a heterogeneous set of L. perenne plants obtained from wild grasslands and cultivars. RESULTS: NIR spectra from dried grass samples were recorded and classified according to the absence or presence of alkaloids, based on reference methods. The best discriminant equations for detection of alkaloids classified correctly 94.4%, 87.5% and 92.9% of plants containing peramine, lolitrem B and ergovaline, respectively. The quantitative NIR equations obtained by modified partial least squares (MPLS) algorithm had coefficients of correlation of 0.93, 0.41, and 0.76 for peramine, lolitrem B and ergovaline respectively. CONCLUSION: NIRS is a suitable tool for qualitative analysis of endophyte alkaloids in grasses and for the accurate quantification of peramine and ergovaline. © 2017 Society of Chemical Industry.

Discrimination of seasonality in cheeses by near-infrared technology.

BACKGROUND: Owing to the importance of the season of collection of milk for cheese quality, a study was made of the usefulness of near-infrared spectroscopy (NIRS) for discriminating the seasonal origin (winter or summer) of milk and quantifying the fat content of cheeses, since fat is one of the components most affected by the season of collection of milk for the elaboration of cheeses. RESULTS: In the internal validation, 96% of samples from winter milk and 97% of samples from summer milk were correctly classified, while in the external validation the prediction rate of samples correctly classified was 92%. Moreover, quantitative models allowed the determination of fat in winter, summer and winter + summer cheeses. CONCLUSION: Rapid prediction of the fat content of cheeses and the seasonal origin (winter or summer) of milk was achieved using NIRS without previous destruction or treatment of samples.

The natural abundance of 13C with different agricultural management by NIRS with fibre optic probe technology.

In the present study the natural abundance of (13)C is quantified in agricultural soils in Mexico which have been submitted to different agronomic practices, zero and conventional tillage, retention of crop residues (with and without) and rotation of crops (wheat and maize) for 17 years, which have influenced the physical, chemical and biological characteristics of the soil. The natural abundance of C13 is quantified by near infrared spectra (NIRS) with a remote reflectance fibre optic probe, applying the probe directly to the soil samples. Discriminate partial least squares analysis of the near infrared spectra allowed to classify soils with and without residues, regardless of the type of tillage or rotation systems used with a prediction rate of 90% in the internal validation and 94% in the external validation. The NIRS calibration model using a modified partial least squares regression allowed to determine the delta(13)C in soils with or without residues, with multiple correlation coefficients 0.81 and standard error prediction 0.5 per thousand in soils with residues and 0.92 and 0.2 per thousand in soils without residues. The ratio performance deviation for the quantification of delta(13)C in soil was 2.5 in soil with residues and 3.8 without residues. This indicated that the model was adequate to determine the delta(13)C of unknown soils in the -16.2 per thousand to -20.4 per thousand range. The development of the NIR calibration permits analytic determinations of the values of delta(13)C in unknown agricultural soils in less time, employing a non-destructive method, by the application of the fibre optic probe of remote reflectance to the soil sample.

Detection and quantification of additives (urea, biuret and poultry litter) in alfalfas by NIR spectroscopy with fibre-optic probe.

The additives (urea, biuret and poultry litter) present in alfalfa, which contribute non-proteic nitrogen, were analysed using near infrared spectroscopy (NIRS) technology together with a remote reflectance fibre-optic probe. We used 75 samples of known alfalfa without additives and 75 samples with each of the additives, urea (0.01-10%), biuret (0.01-10%) and poultry litter (1-25%). Using the discriminant partial least squares (DPLS) algorithm, the presence or absence of the additives urea, biuret and poultry litter is classified and predicted with a high prediction rate of 96.9%, 100% and 100%, obtaining the equations of discrimination for each additive. The regression method employed for the quantification was modified partial least squares (MPLS). The equations were developed using the fibre-optic probe to determine the content of urea, biuret and poultry litter with multiple correlation coefficients (RSQ) and prediction corrected standard errors (SEP (C)) of 0.990, 0.28% for urea, 0.991, 0.29% for biuret and 0.925, 2.08% for poultry litter. The work permits the instantaneous and simultaneous prediction and determination of urea, biuret and poultry litter in alfalfas, applying the fibre-optic directly on the ground samples of alfalfa.

Use of NIRS technology with a remote reflectance fibre-optic probe for predicting major components in cheese.

In the present work the potential of near infra-red spectroscopy technology (NIRS) together with the use of a remote reflectance fibre-optic probe for the analysis of fat, moisture, protein and chlorides contents of commercial cheeses elaborated with mixtures of cow's, ewe's and goat's milk and with different curing times was examined. The probe was applied directly, with no previous sample treatment. The regression method employed was modified partial least squares (MPLS). The equations developed for the cheese samples afforded fat, moisture, protein, and chloride contents in the range 13-52%, 10-62%, 20-30%, and 0.7-2.9%, respectively. The multiple correlation coefficients (RSQ) and prediction corrected standard errors (SEP (C)) obtained were respectively 0.97 and 0.995% for fat; 0.96% and 1.640% for moisture; 0.78% and 0.760% for protein, and 0.89% and 0.112% for chlorides.

Near-infrared spectroscopy (NIRS) with a fibre-optic probe for the prediction of the amino acid composition in animal feeds.

The amino acids alanine, aspartic acid, glutamic acid, glycine, phenylalanine, valine, lysine, proline, and tyrosine present in feeds with different textures (blocks, tablets, granules and flour (meal) and used in different stages of animal feeding regimes (lactation, growth, maintenance, etc.) were analysed using near-infrared reflectance spectroscopy (NIRS) technology together with a remote reflectance fibre-optic probe. The method allows immediate control of the animal feeds without prior sample treatment or destruction through direct application of the fibre-optic probe on the sample. The regression method used was Modified Partial Least Squares (MPLS). The equations developed to determine the amino acid contents of the feeds afforded high values for the RSQ coefficient (0.814-0.963) in all the amino acids with the exception of lysine (0.687). The statistical prediction descriptors SEP, SEP(C) (with values between 0.134 for valine and 0.015 for aspartic acid) and bias indicated that the amino acid values in feeds predicted with NIRS with a fibre optic probe are comparable to those obtained with the chemical ion-exchange HPLC method.

Determination of inorganic elements in animal feeds by NIRS technology and a fibre-optic probe.

In the present work we study the use of near infra-red spectroscopy (NIRS) technology together with a remote reflectance fibre-optic probe for the analysis of the mineral composition of animal feeds. The method allows immediate control of the feeds without prior sample treatment or destruction through direct application of the fibre-optic probe on the sample. The regression method employed was modified partial least squares (MPLS). The calibration results obtained using forty samples of animal feeds allowed the determination of Fe, Mn, Ca, Na, K, P, Zn and Cu, with a standard error of prediction (SEP(C)) and a correlation coefficient (RSQ) of 0.129 and 0.859 for Fe; 0.175 and 0.816 for Mn; 5.470 and 0.927 for Ca; 2.717 and 0.862 for Na; 4.397 and 0.891 for K; 2.226 and 0.881 for P; 0.153 and 0.764 for Zn, and 0.095 and 0.918 for Cu, respectively. The robustness of the method was checked by applying it to 10 animal feeds samples of unknown mineral composition in the external validation.