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Quantum dots (QDs) are crystalline inorganic semiconductor nanoparticles a few nanometers in size that possess unique optical electronic properties vs those of larger materials. For example, QDs usually exhibit a strong and long-lived photoluminescence emission, a feature dependent on size, shape and composition. These special optoelectronic properties make them a promising alternative to conventional luminescent dyes as optical labels in biomedical applications including biomarker quantification, biomolecule targeting and molecular imaging. A key parameter for use of QDs is to functionalize their surface with suitable (bio)molecules to provide stability in aqueous solutions and efficient and selective tagging biomolecules of interest. Researchers have successfully developed biocompatible QDs and have linked them to various biomolecule recognition elements, i.e., antibodies, proteins, DNA, etc. In this chapter, QD synthesis and characterization strategies are reviewed as well as the development of nanoplatforms for luminescent biosensing and imaging-guided targeting. Relevant biomedical applications are highlighted with a particular focus on recent progress in ultrasensitive detection of clinical biomarkers. Finally, key future research goals to functionalize QDs as diagnostic tools are explored.

Breast milk is an optimal food that covers all the nutritional needs of the newborn. It is a dynamic fluid whose composition varies with lactation period. The neonatal units of hospitals have human milk banks, a service that analyzes, stores, and distributes donated human milk. This milk is used to feed premature infants (born before 32 weeks of gestation or weighing less than 1500 g) whose mothers, for some reason, cannot feed them with their own milk. Here, we aimed to develop near-infrared spectroscopy (NIRS) measures for the analysis of breast milk. For this purpose, we used a portable NIRS instrument scanning in the range of 1396–2396 nm to collect the spectra of milk samples. Then, different chemometrics were calculated to develop 18 calibration models with and without using derivatives and the standard normal variate. Once the calibration models were developed, the best treatments were selected according to the correlation coefficients (r2) and prediction errors (SECVs). The best results for the assayed macronutrients were obtained when no pre-treatment was applied to the NIR spectra of fat (r2 = 0.841, SECV = 0.51), raw protein (r2 = 0.512, SECV = 0.21), and carbohydrates (r2 = 0.741, SECV = 1.35). SNV plus the first derivative was applied to obtain satisfactory results for energy (r2 = 0.830, SECV = 9.60) quantification. The interpretation of the obtained results showed the richness of the NIRS spectra; moreover, the presence of specific bands for fat provided excellent statistics in quantitative models. These results demonstrated the ability of portable NIRS sensors in a methodology developed for the quality control of macronutrients in breast milk.

Elemental mass spectrometry is a powerful analytical technique widely established in inorganic analysis. However, despite its quantitative capabilities, it is not yet fully integrated or considered in Life Sciences fields like proteomics. Whereas it is true that ICP-MS has suffered from several instrumental and analytical limitations that have hindered its applicability in protein analysis, significant developments during the last decades have turned ICP-MS into an interesting and, in our opinion, a powerful tool to consider for accurate protein quantification without recourse to specific protein standards. Herein we will try to discuss how these traditional limitations in ICP-MS have been overcome, what further improvements are yet necessary (some of which are shared with MS-based proteomics platforms) and enlighten some of the already existing and potential applications of ICP-MS in absolute quantitative proteomics. SIGNIFICANCE: ICP-MS has the potential to become a complementary tool to help molecular mass spectrometry cope with existing limitations, especially those related to standardization and accuracy, in the absolute proteomics field. It can provide absolute quantification of diverse proteoforms using a single generic compound containing sulfur and/or another target element (e.g., phosphorous). Moreover, its applications in quantitative proteomics are no longer limited to protein standards certification or quantification of simple or purified mixtures. Interestingly, absolute quantification of proteins using ICP-MS is favored when carried out at the intact level, making it very compatible with top-down proteomics approaches. Recent instrumental and methodological advances enable synergic combination of ICP-MS with stablished LC-MS proteomics methodologies, setting the basis for its implementation in quantitative proteomics workflows.

There is a growing interest in the development of sensitive, portable, and low-cost instrumentation for optical chemical (bio)sensing. Such instrumentation can allow real-time decision-making for industry, farmers, and researchers. The combination of optical fiber schemes, luminescence spectroscopy techniques, and new materials for sensor immobilization has allowed the growth of optical sensors. This article focuses on the development of low-cost optoelectronic instrumentation and measurement strategies for optical chemical (bio)sensing. Most of the articles in this field have focused on the chemical sensors themselves, although few have covered the design process for optoelectronic instrumentation. This article tries to fill this gap by presenting designs for real applications, as carried out by the authors. We also offer an introduction to the optical devices and optical measurement techniques used in this field to allow a full understanding of the applications.

MicroRNAs (miRNAs) are a novel class of biomarkers that can be present in different biological samples, which can be related to many diseases. Controlled aggregation of gold nanoparticles (GNPs) in the presence of miRNAs can be used to perform a rapid detection of miRNAs based on color change. Sometimes the color change can be observed with the naked eye; however, this is not always possible. This work proposes the design of a novel instrumental prototype that allows performance of simultaneous transmittance measurements at different wavelengths. Once the signals are obtained, the system calculates the 525nm/650nm transmittance ratio and the miRNA concentration, displaying and recording the results in a smartphone. This prototype has been validated to detect the miRNA "miR146a," which is found in higher concentrations in milk and tissues in cases of bovine mastitis.

A portable instrumental system was designed for the routine environmental monitoring of toxic volatile organic compounds (VOCs) in atmospheric conditions based on changes in the photoluminescence emission of semiconductor nanoparticles (quantum dots) entrapped in a sol-gel matrix as the solid sensing material. The sol-gel sensing material displayed a long-lived phosphorescent emission, which is quenched in the presence of trace levels of a volatile organic compound (acetone) in gaseous atmospheres. The developed instrument could measure and process the changes in the photoluminescence of the sensing material after exposure to gaseous acetone. The developed prototype device consists of a deep-ultraviolet ligtht-emitting diode (UV LED), which excites the chemical sensing material, an optical filter to remove scattered light and other non-desirable wavelengths, a photomultiplier tube (PMT) to convert the phosphorescence emission of the sensor phase to an electrical signal, and a microcontroller to correlate the signal with the analyte concentration. The developed prototype was evaluated for its ability to measure low levels of gaseous acetone in contaminated atmospheres with high sensitivity (detection limit: 9 ppm). The obtained results show the feasibility of this type of instrument for environmental analytical control purposes.

Gold nanoparticles of different sizes have been synthesized and surface-functionalized with selected RNA probes in order to develop a rapid, low-cost and sensitive method for detection of microRNA146a (miR146a). The strategy is based on the change of colour that can be observed visually after aggregation of the RNA modified-gold nanoparticles (AuNPs) in presence of miR146a. Experimental conditions have been carefully selected in order to obtain a good sensitivity that allows to perform visual detection of microRNA at the nM level, achieving a detection limit of 5 nM. Good repeatability and selectivity versus other sequences that only differ from miR146a in 3 bases was achieved. miR146a has been described as one of the main microRNA involved in the immune response of bovine mastitis, being expressed in tissue, blood and milk samples. The method was successfully applied to the detection of miR146a in raw cow milk samples. The present scheme constitutes a rapid and low-cost alternative to perform highly sensitive detection of microRNA without the need of instrumentation and amplification steps for the early detection of bovine mastitis in the agrofood industry. Graphical abstract Schematic representation of the assay based on aggregation of RNA-modified gold nanoparticles (blue) in presence of microRNA146a generating a dark blue spot onto a solid support, versus a pink spot observed in absence of miR146a due to dispersed gold nanoparticles (red).

In this work, novel silver sulphide quantum dots (Ag2S QD) are electrochemically quantified for the first time. The method is based on the electrochemical reduction of Ag+ to Ag0 at −0.3 V on screen-printed carbon electrodes (SPCEs), followed by anodic stripping voltammetric oxidation that gives a peak of currents at +0.06 V which represents the analytical signal. The optimized methodology allows the quantification of water-stabilized Ag2S QD in the range of approximately 2 × 109–2 × 1012 QD·mL−1 with a good reproducibility (RSD: 5%). Moreover, as proof-of-concept of relevant biosensing application, Ag2S QD are evaluated as tags for Escherichia coli (E. coli) bacteria determination. Bacteria tagged with QD are separated by centrifugation from the sample solution and placed on the SPCE surface for quantitative analysis. The effect of two different Ag2S QD surface coating/stabilizing agents on both the voltammetric response and the bacteria sensing is also evaluated. 3-mercaptopropionic acid (3-MPA) is studied as model of short length coating ligand with no affinity for the bacteria, while boronic acid (BA) is evaluated as longer length ligand with chemical affinity for the polysaccharides present in the peptidoglycan layer on the bacteria cells surface. The biosensing system allows to detect bacteria in the range 10−1-103 bacteria·mL−1 with a limit of detection as low as 1 bacteria·mL−1. This methodology is a promising proof-of-concept alternative to traditional laboratory-based tests, with good sensitivity and short time and low cost of analysis.

3 páginas, 1 tabla, 1 figura., Financial support for this study was provided by the following projects: GRUPIN-IDI/2018/000237 (Principado de Asturias) and RTA2015-00061/AEI/FEDER (Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria)

This work is focused on development of a quick and simple to use analytical methodology for on-site monitoring the fatty acid (FA) profile in raw milk at farm level by using a near infrared handheld spectrometer. This novel methodology was developed using a total of 108 liquid milk samples, scanned at room temperature by NIRS without pre-treatment, and analyzed by GC–MS for reference data. Calibration was carried out by multivariate regression combining math pre-treatments and Partial Least Square with internal and external validation. Calibration models displayed good predictive capacity for total saturated, monounsaturated (MUFA) and polyunsaturated FA (PUFA) with high coefficients of determination of cross validation (R2cv > 0.8). Good results were also obtained for prediction of individual FA: caproic, capric, lauric, miristic, palmitic and arachidic as well as for unsaturated FA: oleic, conjugated linoleic acid and omega-6 acids with R2cv values ranged between 0.91- 0.73. Validation statistics have confirmed that the highest R2v (coefficient of determination of external validation) values to quantify FA were for PUFA, linolenic acid (R2v = 0.92), caproic acid and MUFA (R2v = 0.87). These results establish that a profitable classification of milk can be carried out at farm level by including a fatty acid composition labeling.

Simple Summary Dairy cattle is a source of ammonia because only 25–35% of the dietary nitrogen is used for the synthesis of milk, and the remainder is excreted through feces and urine. A reduction in dietary nitrogen is an effective way to decrease nitrogen excretions and subsequent ammonia emissions. However, this reduction should not induce a decrease in the potential yield of the cows. On the other hand, legumes are more susceptible than grasses to undergo proteolysis in the silage process due to their higher protein content. However, not all legumes have the same rate of proteolysis rate. With the main objective of improving the quality of the slurry to be used as organic fertilizer, two sequential experiments were carried out. In the first, it was intended to determine the optimal level of dietary nitrogen intake necessary for high-production dairy cows. Once this level was established, two legume silages with different proteolysis rates were evaluated. In conclusion, dairy cows producing more than 30 kg of milk per day can meet their needs with diets with 13% of protein, reducing nitrogen losses through urine. The main pathway for the excretion of dietary nitrogen provided by legume silage is the urine, and the protein of field pea silage is metabolized towards ammonia production to a larger extent than the protein of faba bean silage. Abstract Less than 30% of dairy cattle’s nitrogen ingested is retained in milk. Therefore, large amounts of nitrogen can be excreted in manure and urine with a potential environmental impact. In addition, some legume forages can be more susceptible to proteolysis during the silage process than grasses, and dairy cows fed these legume silages would excrete a larger quantity of nitrogen in slurry. The objectives of this work were to evaluate the amount of nitrogen excretion in dairy cows fed different protein levels and legume silages with a view to improve the slurry quality as a co-product that can be used as fertilizer. Two double 3 × 3 Latin square trials were carried out in order to study three different protein levels (high, medium, and low) and three different silages (grass, faba bean, and field pea). Dry matter intake, milk production, and composition were not affected by treatments. The excretion of ammonia-N in the urine was almost four times lower in the diet with the lowest protein level. The ammonia-N in the urine was twice as high with the pea silage than faba bean and grass silages. In conclusion, the diet containing 13% of protein meets the protein requirement for lactating cows producing 31 kg daily, with low nitrogen excretion in the urine, and the main pathway for the excretion of surplus nitrogen from legume silages is through urine and the metabolization of pea silage protein goes toward ammonia-N.

The real time knowledge of dairy milk composition can be used as a tool to guarantee milk quality and safety, offering additional information for dairy producers and consumers. To carry out these in situ analyses, methodologies based on Near Infrared (NIR) portable sensors have a great potential as an advisory tool. The main goals of the present work have been to develop a methodology using a hand-held portable NIR spectrophotometer to collect raw milk spectra, including the development of calibration models for the analysis of protein, fat and solids-non-fat (SNF) of raw milk and further to transfer the developed models to another portable unit. A total of 542 fresh milk samples were scanned over the NIR spectral range (1600–2400 nm) using a hand-held MicroPhazir™ (MP) NIR spectrometer and different instrumental configurations. The best results for repeatability and reproducibility calculated as root mean squared (RMS) were obtained using a 17 mm cuvette thickness. The displayed predictive ability of calibration models measured as Standard error of prediction/Standard error of cross validation were 0.96; 0.72 and 0.83 for fat, protein and SNF contents, respectively. For cloning purposes an additional MP unit (satellite) has been used. A standardization set of 10 samples enabled standardization of both instruments. After applying standardization matrix, Standard error of differences between master and satellite reached great reduction, 68% for fat, 66% for protein and 54% for SNF. Moreover, the demonstrated ability of sharing calibration models among several units is essential for implementation of portable instruments for in-situ analysis to provide indicators of milk composition at farm level.

Significant advances achieved in different sensor technologies and computer processing data have made possible to respond the needs of livestock sector, providing precise and rapid information on feed composition, being an alternative to real time quality control on compound feed the use of handheld NIRS sensors. This work aimed to evaluate two hand-held portable NIR spectrophotometers for on-site and real time analysis of nutritive parameters in raw compound feed: Phazir 1624 Polychromix Inc (PhIR) and MicroNIRTM 1700 by JDSU (MICRO). For computing data, different combinations of pre-treatments and multivariate statistical methods have been assayed to extract the valuable information of spectra data and to develop appropriate calibrations. The calibration models displayed greatest predictive capacity for Crude Protein (CP), Crude Fiber (CF) and Starch (STCH) and the determination coefficients of cross validation were 0.90–0.88 for CP, 0.85–0.91 for CF, 0.89–0.88 and 0.89–0.91 for STCH using PhIR and MICRO instruments respectively. Dry Matter showed the lowest determination coefficients of cross validation 0.67–0.73. Accuracy achieved 99–101% for both NIRS instruments and no differences were found when applying tstudent-test comparing reference and predicted data. Results obtained with both instruments were compared by using standard deviation and not significant differences were observed at the 5% level. Results so far have demonstrated the potential of these handheld NIRS instruments proposed here to estimate the individual compound feeds composition changes at farms level instantly, time avoiding the disadvantage of moving the samples to the lab.

International Conference on Sensing and Instrumentation in IoT Era, ISSI (2019. Lisbon, Portugal), The financial support of the Spanish Government (Ministry of Economy, Industry and Competitiveness), Project RTA2015-00061-C02-02, is gratefully acknowledged.

Near infrared (NIR) emitting Ag2S quantum dots have been synthesized, characterized and evaluated for chemical sensing applications. After their optical characterization, it was observed that the Ag2S quantum dots present both, excitation and emission in the NIR region, and an excellent quantum yield of 33.2%. These features are of great value for many biological applications, since autofluorescence of biological tissues or cells is minimized, and also for environmental applications, where other fluorescent concomitant species with excitation and emission in the ultraviolet-visible region might be present. Different purification procedures were evaluated in order to obtain a stable and homogeneous population of nanoparticles, which is necessary to perform quantitative analysis (e.g.: mass spectrometry-based applications), as well as to obtain a narrow NIR emission spectrum for optical applications. Comprehensive characterization using X-ray diffraction, transmission electron microscopy, and asymmetric flow field flow fractionation coupled to inductively coupled plasma-mass spectrometry has been performed to obtain parameters not easily achieved and of great interest in different research areas, such as the nanoparticle concentration NIR-emitting nanoparticles, and the surface ligand density, which directly affects to the interactions of the nanoparticles with their close environment, including unspecific adsorptions, cellular uptake, macrophage interaction, etc. Finally, the capability for sensing analytes of environmental interest based on direct-interactions of a reactive compound with the surface of the nanoparticle has been also evaluated. Quenching of the NIR emission upon interaction of the Ag2S quantum dots with cyanide ions was observed. Hence, a rapid, selective and highly sensitive methodology was developed for the detection of cyanide in natural waters.

Having rapid and reliable knowledge of the quality of dairy farm forage is crucial for technicians and producers. However, the necessary equipment to fulfil these requirements is oftentimes expensive and complex to use. This work presents a portable instrument system to analyze the nutritional values of dairy farm forage using near-infrared spectrometry (NIRS) techniques. Using the Internet of Things (IoT) tools, data are sent to the cloud for processing; following this, they are accessible to any device. To analyze the nutritional values of dairy farm forage a chemometric model and implemented the instrument to understand the relationship between the measured spectrum and the concentration of the substances of interest was developed. The quality of the proposed methodology was validated by comparing reference and NIR data by using the statistic Standard Error of Calibration.

Three trials were conducted to study the effect of including in the ration of dairy cows the intercrop faba bean-rapeseed or Italian ryegrass silages on feed intake, milk quality and enteric methane emissions. Ten lactating Holstein cows, randomly allocated in two groups, were used in each trial in a crossover design. Dry matter intake and milk yield were recorded daily for each data period. Milk was sampled for analyses three times per period. Enteric methane emission was estimated applying a model that includes bodyweight, dry matter intake, nutritional values of feeds and milk composition. Concentrate and grass intake did not differ between treatments, while total mixed ration intake was higher with Italian ryegrass silage. Milk yield and protein concentration also increased with the Italian ryegrass treatment. The milk urea concentration was higher with the faba bean and rapeseed diet. Fatty acid profiles were affected by feeding strategy. Thereby, t11 18:1, c9 18:1 and total unsaturated fatty acid were higher when the intercrop silage was fed. As consequence, atherogenicity index and thrombogenicity index were lower under that treatment. Estimated enteric methane emissions did not differ between treatments. In conclusion, faba bean-rapeseed intercrop can be an alternative to Italian ryegrass to feed dairy cows. The rations formulated with the intercrop silage have potential to improve the milk fatty acid content, and show healthier profiles for consumers. However, it would be useful to study the protein content of these crops to avoid possible excess urea in milk and to maintain sustainable milk yield and milk protein content.

Fatty acids (FAs) play many essential roles in biological systems, and they are the aim of different research studies due to their benefits on human health. Milk and dairy products contribute significantly to the consumption of FAs in the human diet. In consequence, manipulation of FA composition of cows’ milk via nutritional strategies has been an important target for the dairy industry and a challenge from an analytical point of view. Milk FA composition is complex, and their analysis involves multiple steps (extraction procedure, methylation, FA methyl ester extraction and gas chromatography (GC) determination) that turn it into a tedious and time-consuming procedure. In recent years, some efforts have been made to develop an analytical approach with simultaneous extraction and derivatization of FAs. In this sense, microwave (MW)-assisted digestion and extraction methods have been used for many years and today can be considered standard operating procedures in many laboratories. It is a powerful tool for different analytical methodology development. This study is focused on one-step extraction/transmethylation MW-assisted methodology feasibility for FA analysis in milk compared to a reference method.

Sixty-one intact meat samples from Asturcelta autochthonous swine breed were scanned in the slaughterhouse in reflectance mode. A handheld microelectromechanical system digital transform (Phazir1624, Polychromix Inc.), with a window sampling area of 0.8 × 1 cm and wavelengths ranging from 1,600 to 2,400 nm, was used. With the spectra database recorded were developed different chemometrical models assaying first and second derivatives as math treatment and standard normal variate (SNV) and multiplicative scatter correction for minimizing scattering effect. The greatest predictive capacity was achieved after applying SNV and first derivative for moisture, intramuscular fat (IMF) content, and pH parameters and second derivative for CIE L*, a*, b* colorimetric values, and the Warner-Bratzler force (instrumental texture). The coefficients of determination for calibration ranged from 0.63 to 0.89. The ratio between the standard error of the laboratory and the standard error of calibration ranged from 0.8 to 2.5 for all parameters (1.7 on average) with the exception of b and pH with ratios of 3.5 and 4.1, respectively. The statistical values obtained for the models developed to estimate IMF, CIE L*, a*, b*, moisture, and pH, displayed acceptable predictive capacity. For instrumental texture, the model could be able to discriminate among tender, medium, and hard meat in carcasses for characterization slaughter purposes. © 2013 Springer Science+Business Media New York.

As a first step in a project whose aim is to implement near infrared (NIR) analysis of animal feed on the farm, the present work has examined the possibility of transferring undried grass silage calibrations for dry matter, crude protein, and neutral detergent fiber from a dispersive laboratory NIR instrument (Foss NIRSystem 6500) to a diode array on-site NIR instrument (Zeiss Corona 45 visNIR 1.7). Because the samples are complex and heterogeneous and have high humidity levels it is not easy to establish good calibrations, and it is even more of a challenge to transfer them. By cutting the spectral range to 1100–1650 nm and treating with first or second derivative followed by standard normal variate (SNV) scatter correction, it was possible to obtain very similar spectra from the two instruments. To make the transfer, two approaches were tried. Simply correcting the Corona spectra by subtracting the mean difference spectrum from a transfer set met with only limited success. Making a calibration on the Foss using a calibration set of 503 samples with spectra orthogonalized to the all the difference spectra in the transfer set of 10 samples resulted in a successful transfer for all three calibrations, as judged by performance on two prediction sets of size 22 and 29. Measuring 5 replicate subsamples with the Corona allows it to see a similar surface area to that of 3 replicates in the Foss transport cell, and it is suggested that this is an appropriate level of replication for the Corona.

In recent years, consumer attitudes toward fat of animal origin have changed owing to findings that some milk fatty acids (FAs) are positive for human health, especially conjugated linolenic acid andn-3 FAs. Accordingly, the manipulation of the fat content and FA composition of cows’ milk via nutritional strategies has been an important target for the dairy industry in many countries. Twenty commercial Holstein–Friesian dairy herds of Asturias (northern Spain) with 1106 dairy cows were examined in order to evaluate milk FA profiles under different management systems. These herds were divided into three groups according to management: (1) indoor herds: cows feeding indoors, (2) mixed herds: indoor management system but with at least 6 h of grazing outdoors and (3) outdoor herds: cows allowed 6–18 h of grazing per day. Milk from the indoor herds exhibited the highest concentration of fat (3.57%;P⩽0.01), protein (3.14%;P⩽0.001), lactose (4.76%;P⩽0.01) and urea (29.4 mg dl−1;P⩽0.01). The milk of outdoor herds had a lower (P⩽0.05) content of short-chain FAs than that of the indoor and mixed herds (10.89 versus 11.52 and 11.35 g 100 g−1FA). The milk of the indoor herds had higher concentrations of saturated fatty acids (SFA) (67.56 g 100 g−1FA;P⩽0.001) and palmitic and palmitoleic acids (30.16 and 1.82 g 100 g−1FA, respectively), while that of the mixed and outdoors herds had higher concentrations of unsaturated fatty acids (UFA) (34.58 g 100 g−1FA;P⩽0.001) and long-chain FAs, especially stearic (13.89 g 100 g−1FA;P⩽0.01), vaccenic (2.77 g 100 g−1FA;P⩽0.001), conjugated linoleic (0.92 g 100 g−1FA;P⩽0.001) and linolenic (0.42 g 100 g−1FA;P⩽0.001) acids. Results from this study suggest that the incorporation of forage and pasture in the diet of dairy cows can improve the FA profile of milk.

Traceability of animal products has become a priority for governments of the developed countries as a guarantee of food safety. Near infrared microscopy (NIRM) has been proposed as an alternative technology to detect and quantify banned ingredients in feedstuffs. The great advantage of this technique is its objectivity, whilst retaining the sensitivity of classic microscopy. The aim of this work was to build an NIRM reference spectral library on animal feed, consisting of samples of animal feed ingredients and possible contaminants, and to assess its ability to discriminate between ingredients using an internal cross-validation. A total of 48,899 spectra were measured on 229 samples representing 30 different ingredients. The method chosen for classification was K-nearest-neighbours (KNN) using first derivative spectra. Although the results showed an overall classification error of 35.88%, there was good discrimination between ingredients of animal and vegetable origin. There was some confusion between similar vegetable ingredients but this is unimportant.

Grazing cows could produce milk with a higher proportion of polyunsaturated fatty acids, which is beneficial to human health, compared with non-grazing cows, though grazing alone could compromise milk production. Under oceanic climate conditions, a study involving 15 dairy cows, fed total mixed ration (TMR)ad libitumin combination with different grazing times of 12 h (TMR12), 6 h (TMR06) and zero grazing time (TMR00) with the aim to evaluate different strategies on the fatty acids profile of milk and milk production. No differences were seen between the treatments with respect to milk yield (34·4±6·3 kg/d) or milk protein content (30·4±1·8 g/kg). The milk produced by the TMR12 cows had less total fat (36·2 vs. 38·2 g/kg) and saturated fatty acid (FA, 69·39 vs. 71·44 g/100 g FA) than that produced by the TMR00 cows. The concentration of vaccenic acid in the TMR06 and TMR12 milk was twice that of the TMR00 milk (4·22, 4·09 and 2·26 g/100 g FA respectively). Linear increases in conjugated linoleic (CLA) and linolenic acids were observed with increasing grazing time. Pasture was an important source of FA especially C18:3 for TMR06 and TMR12 cows. Under oceanic climatic conditions, the grazing of dairy cows as a complement to feeding with TMR can improve the FA profile of milk and increase its CLA content.

Milk fatty acid (FA) profiles of lactating Holstein dairy cows were determined to evaluate the effects of parity (primiparous or multiparous) and the grazing time (0, 6 and 12 h), complementary to a total mixed ration, in a 2 × 3 factorial design. The interaction of parity with grazing time did not affect milk production and protein content; however, dry matter intake of herbage, total dry matter intake and milk fat content were affected by the interaction (P < 0.05). Both factors affected the milk concentration of monounsaturated FAs. The 18 : 3 cis9 cis12, 18 : 2 cis9 trans11, and 18 : 3 cis9 cis12 cis15 concentrations in milk were higher (P < 0.05) in primiparous cows. Milk 18 : 2 cis9 trans11 concentration in primiparous cows with 12 h grazing (1.17 g/100 g FA) was 44% and 57% higher than with 6 h and 0 h grazing (0.66 and 0.50 g/100 g FA, respectively). Similar pattern was observed for 18 : 3 cis9 cis12 cis15. The milk 18 : 1 trans11 concentration was higher (P < 0.05) with 6 h and 12 h grazing than without grazing. In conclusion, the parity would appear to mainly affect the proportion of short-chain FAs, and had an important influence on the proportions of 18 : 2 cis9 trans11, and 18 : 3 cis9 cis12 cis15. These FAs and also 18 : 1 trans11 were modified by grazing time.

To guarantee feed quality and safety the development and improvement of analytical methods for feed authentication and detection of contaminants is fundamental. Near infrared reflectance microscopy (NIRM) has been investigated as an alternative method to contribute to control systems for feed materials. The major task is the need to build NIRM reference spectral libraries that must represent the variability in feed ingredients. The aim of the present work was to evaluate the performance of a NIRM reference spectral library on animal feed, with external samples of animal feed ingredients and possible contaminants such as processed animal proteins, and in particular to assess its ability to identify ingredients in mixtures. Three external sample sets were used: (A) artificial mixtures, (B) synthetic mixtures and (C) synthetic binary mixtures. The prediction and repeatability results for set A, in which the spectra are from pure ingredients, were very good for both animal and vegetable ingredients and confirm that the spectral library is very good at identifying spectra from pure ingredients. For sets B and C, in which the spectra were measured on mixtures, the prediction results were very disappointing compared with the artificial samples. This means that a strategy that tries to match the spectra taken from a mixture with those of pure ingredients is unlikely to meet with much success. It is possible that an interpolation between pure ingredients for suitably chosen spectral ranges may provide a way to extend this system to mixtures, including mixtures of several ingredients.

The establishment of fast and non-destructive methods for the evaluation of quality and safety of raw grains is being demanded nowadays to avoid toxic substance presence. Aflatoxin B1 (AFB1) has been recognised by the International Agency of Research on Cancer as a group 1 carcinogen for animals and humans and the EU Official Journal has established action levels for AFB1 presence in all feed materials between 5 and 20 ppb. Near infrared spectroscopy (NIRS) is an excellent candidate for a rapid and low-cost method for the detection of aflatoxins in cereals. This study assesses the utility of NIRS for rapid detection of mycotoxigenic fungi as AFB1. A total of 152 samples were involved and analysed for aflatoxin content. The results of spectroscopic models developed have demonstrated that NIRS technology is an excellent alternative for fast AFB1 detection in cereals. The best predictive model to detect AFB1 in maize was obtained using standard normal variate and detrending (SNVD) as scatter correction ( r 2 = 0.80 and 0.82; SECV = 0.211 and 0.200 for grating and FT-NIRS instruments, respectively). In the case of barley, the best predictive model was developed using SNVD on the dispersive NIRS instrument ( r 2 = 0.85 and SECV = 0.176) and using spectral data as log 1/ R for FT-NIRS ( r 2 = 0.84 and SECV = 0.183).