Colostrum Quality Assessment in Dairy Goats: Use of an On-Farm Optical Refractometer.

Failure of passive immunity transfer is one of the main causes of increased susceptibility to infectious agents in newborn kids. To ensure successful transfer of passive immunity, kids need to be fed high-quality colostrum, containing an adequate concentration of IgG. This work evaluated the quality of colostrum obtained in the first 3 days postpartum from Malagueña dairy goats. The IgG concentration in colostrum was measured using an ELISA as a reference method, and it was estimated by optical refractometer. Colostrum composition in terms of fat and protein was also determined. The mean concentration of IgG was 36.6 ± 2.3 mg/mL, 22.4 ± 1.5 mg/mL and 8.4 ± 1.0 mg/mL on days 1, 2 and 3 after parturition, respectively. Brix values obtained using the optical refractometer were 23.2%, 18.6% and 14.1% for days 1, 2 and 3, respectively. In this population, 89% of goats produced high-quality colostrum with IgG concentrations of >20 mg/mL on the day of parturition, but this percentage declined dramatically over the following 2 days. The quality of the fresh colostrum estimated with the optical refractometer was positively correlated with those obtained using ELISA (r = 0.607, p = 0.001). This study highlights the importance of feeding first-day colostrum to newborn kids and demonstrates that the optical Brix refractometer is suitable for the on-farm estimation of IgG content in colostrum.

Optimisation of the predictive ability of NIR models to estimate nutritional parameters in elephant grass through LOCAL algorithms.

Elephant grass is a tropical forage widely used for livestock feed. The analytical techniques traditionally used for its nutritional evaluation are costly and time consuming. Alternatively, Near Infrared Spectroscopy (NIRS) technology has been used as a rapid analysis technique. However, in crops with high variability due to genetic improvement, predictive models quickly lose accuracy and must be recalibrated. The use of non-linear models such as LOCAL calibrations could mitigate these issues, although a number of parameters need to be optimized to obtain accurate results. The objective of this work was to compare the predictive results obtained with global NIRS calibrations and with LOCAL calibrations, paying special attention to the configuration parameters of the models. The results obtained showed that the prediction errors with the LOCAL models were between 1.6 and 17.5 % lower. The best results were obtained in most cases with a low number of selected samples (n = 100-250) and a high number of PLS terms (n = 20). This configuration allows a reduced computation time with high accuracy, becoming a valuable alternative for analytical determinations that require ruminal fluid, which would improve the welfare of the animals by avoiding the need to surgically prepare animals to estimate the nutritional value of the feeds.

Transferring a large data library of fresh total mixed rations from a benchtop to 2 portable near-infrared spectrometers for on-farm real-time decisions.

This study was carried out using a spectral database consisting of 394 samples of fresh total mixed ration (TMR) from dairy farms located at Northern Spain. Cloning sets of different size and structure were evaluated for the transfer of the large TMR spectral database obtained on a Foss NIRSystems monochromator to 2 different portable near-infrared devices: one diode array instrument and another based on linear variable filters. The cloning matrix that produced the best matching between instruments was then used to transfer the TMR spectral library to the 2 portable instruments. Once the database had been transferred, calibration equations were developed to compare the predictive ability of the equations obtained in the benchtop and portable instruments. In comparison with the monochromator predictive ability, the calibration equations developed with the near-infrared portable instruments displayed a high and similar accuracy for most of the studied parameters related to TMR composition, enabling their use for predicting TMR quality at the farm level.

Recent Advances in Portable and Handheld NIR Spectrometers and Applications in Milk, Cheese and Dairy Powders.

Quality and safety monitoring in the dairy industry is required to ensure products meet a high-standard based on legislation and customer requirements. The need for non-destructive, low-cost and user-friendly process analytical technologies, targeted at operators (as the end-users) for routine product inspections is increasing. In recent years, the development and advances in sensing technologies have led to miniaturisation of near infrared (NIR) spectrometers to a new era. The new generation of miniaturised NIR analysers are designed as compact, small and lightweight devices with a low cost, providing a strong capability for on-site or on-farm product measurements. Applying portable and handheld NIR spectrometers in the dairy sector is increasing; however, little information is currently available on these applications and instrument performance. As a result, this review focuses on recent developments of handheld and portable NIR devices and its latest applications in the field of dairy, including chemical composition, on-site quality detection, and safety assurance (i.e., adulteration) in milk, cheese and dairy powders. Comparison of model performance between handheld and bench-top NIR spectrometers is also given. Lastly, challenges of current handheld/portable devices and future trends on implementing these devices in the dairy sector is discussed.

Fraud Detection in Batches of Sweet Almonds by Portable Near-Infrared Spectral Devices.

One of the key challenges for the almond industry is how to detect the presence of bitter almonds in commercial batches of sweet almonds. The main aim of this research is to assess the potential of near-infrared spectroscopy (NIRS) by means of using portable instruments in the industry to detect batches of sweet almonds which have been adulterated with bitter almonds. To achieve this, sweet almonds and non-sweet almonds (bitter almonds and mixtures of sweet almonds with different percentages (from 5% to 20%) of bitter almonds) were analysed using a new generation of portable spectrophotometers. Three strategies (only bitter almonds, bitter almonds and mixtures, and only mixtures) were used to optimise the construction of the non-sweet almond training set. Models developed using partial least squares-discriminant analysis (PLS-DA) correctly classified 86-100% of samples, depending on the instrument used and the strategy followed for constructing the non-sweet almond training set. These results confirm that NIR spectroscopy provides a reliable, accurate method for detecting the presence of bitter almonds in batches of sweet almonds, with up to 5% adulteration levels (lower levels should be tested in future studies), and that this technology can be readily used at the main steps of the production chain.

An innovative non-targeted control system based on NIR spectral information for detecting non-compliant batches of sweet almonds.

Nowadays, there is growing awareness about the need to develop new methodologies to fight against deliberate fraud. This study explored the use of near infrared spectroscopy (NIRS) as an instantaneous, non-targeted method for detecting non-compliant products; in this case, when used to detect sweet almond batches adulterated with bitter almonds. For this purpose, we simulated the adulteration of batches by preparing four different types of mixed samples which contained 5%, 10%, 15% and 20% of bitter almonds, respectively, using 90 samples of sweet almonds and 50 samples of bitter almonds. For each of the adulteration percentages, 21 samples were produced. The samples were analysed using the Aurora and the Matrix-F spectrophotometers. The procedure initially constructed the desired standard or target using only the spectral information provided by the sweet almond population (control population). To achieve this, after principal components analysis, the spectral warning and action limits were calculated using the n-dimensional statistic Mahalanobis global distance. Next, the spectral distances from the product standard defined for those samples not belonging to the control population, including the adulterated sweet almonds, were calculated and represented as Shewhart control charts. The implementation of NIRS technology throughout the almond supply chain enabled to identify 87% (73/84) of the adulterated sweet almond batches. These findings suggest that NIRS technology and the use of spectral distances could enable to establish an innovative, non-targeted control system based only on spectral information to assess almond batches. This system allows to carry out conformity tests both in situ and online of the batches of almonds received and processed in the industry, as well as establishing fast, cost-efficient anti-fraud alert systems, which would help to reduce the number of batches to be analysed by expensive and time-consuming confirmatory methods.

Reduction of the Number of Samples for Cost-Effective Hyperspectral Grape Quality Predictive Models.

Developing chemometric models from near-infrared (NIR) spectra requires the use of a representative calibration set of the entire population. Therefore, generally, the calibration procedure requires a large number of resources. For that reason, there is a great interest in identifying the most spectrally representative samples within a large population set. In this study, principal component and hierarchical clustering analyses have been compared for their ability to provide different representative calibration sets. The calibration sets generated have been used to control the technological maturity of grapes and total phenolic compounds of grape skins in red and white cultivars. Finally, the accuracy and precision of the models obtained with these calibration sets resulted from the application of the selection algorithms studied have been compared with each other and with the whole set of samples using an external validation set. Most of the standard errors of prediction (SEP) in external validation obtained from the reduced data sets were not significantly different from those obtained using the whole data set. Moreover, sample subsets resulting from hierarchical clustering analysis appear to produce slightly better results.

Routine NIRS analysis methodology to predict quality and safety indexes in spinach plants during their growing season in the field.

Cultural practices and harvesting in spinach plants should be based not only on subjective indexes such as freshness and green colour, which are both related with the visual appearance of the plants, but also on objective indexes that can be quantified non-destructively. The aim of this research was to develop a methodology based on the use of near infrared spectroscopy to monitor routinely the growth process of the spinach plants in the field. Using the MicroNIR™ OnSite-W spectrophotometer, which is ideally suited for in situ analysis, 261 spinach plants were analysed. Initially, calibration models for dry matter, soluble solid and nitrate contents were developed using 1 spectrum per plant for dry matter content, and nine spectra per plant for the other two parameters. These models were then validated using the same number of spectra per plant as for calibration purposes. After that, to establish a procedure more suitable to routine analysis in the field, the models were validated with only one spectrum per plant and the suitability of the predictions was measured considering the global and neighbourhood Mahalanobis distances, whose control limit values were defined as inferior to 4.0 and 1.0, respectively. The results showed that once the calibration models were developed, only one spectrum per plant was enough to predict dry matter and nitrate contents successfully. Therefore, the methodology developed will allow us to monitor in real time the complete growth process and to take decisions about spinach cultivation based on internal quality and safety indexes.

Probabilistic classification models for the in situ authentication of iberian pig carcasses using near infrared spectroscopy.

Iberian pig ham is one of several high value European food products that are the subject of significant attempts at fraud because of the high price differences between commercial categories. Iberian pig products are classified by the Spanish regulations into different categories, mainly depending on the feeding regime during the fattening phase and the race involved, being of Premium quality those products obtained from the animals fed with acorns and other natural resources. Most of the previous NIRS studies related to the Iberian pig have involved the use of at-line instruments to predict quantitative quality parameters. This paper explores the use of the NIR spectra (369 for training and 199 for validation) to classify samples according to the categories Premium (animals fed with acorn) and Non Premium (animals fed with compound feeds), using a MicroNIR™ Pro1700 microspectrometer to analyse individual carcasses in situ at the slaughterhouse line. Four discriminant methods were explored: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), Kernel Bayes and Logistic Regression. These are all discriminant methods that naturally produce classification probabilities to quantify the uncertainty of the results. Rules were tuned and methods compared using both classification error rates and a probability scoring rule. LDA gave the best results, attaining an overall accuracy of 93% and providing well-calibrated classification probabilities.

Welfare Quality® for dairy cows: towards a sensor-based assessment.

This Research Reflection addresses the possibilities for Welfare Quality® to evolve from an assessment method based on data gathered on punctual visits to the farm to an assessment method based on sensor data. This approach could provide continuous and objective data, while being less costly and time consuming. Precision Livestock Farming (PLF) technologies enabling the monitorisation of Welfare Quality® measures are reviewed and discussed. For those measures that cannot be assessed by current technologies, some options to be developed are proposed. Picturing future dairy farms, the need for multipurpose and non-invasive PLF technologies is stated, in order to avoid an excessive artificialisation of the production system. Social concerns regarding digitalisation are also discussed.

In situ ripening stages monitoring of Lamuyo pepper using a new-generation near-infrared spectroscopy sensor.

BACKGROUND: Near-infrared spectroscopy (NIRS) was used as a nondestructive sensor to assess the quality of freshly harvested Lamuyo peppers. One hundred and forty-four Lamuyo peppers, which were in a range of colors (green, chocolate, orange, and red) when harvested, were analyzed. In this study, the evolution of the main quality parameters during the harvest period was analyzed. Additionally, NIRS predictive models using a portable manual spectrophotometer to evaluate quality parameters together with color index were developed. Moreover, two procedures for taking near-infrared spectra were tested: (1) static, where point spectral readings were taken of around the equator of the fruit; (2) dynamic, where spectra were taken by scanning the entire length of the pepper. RESULTS: Green peppers and those harvested at the beginning of the campaign presented significantly lower values (P < 0.05) of dry matter, soluble solid contents, and titratable acidity, whereas those with red coloration and those harvested at the end of the campaign showed significantly higher values of these three quality parameters (P < 0.05). The predictive capacity of the NIRS models showed that the static mode proved to be the most suitable for measuring the quality of Lamuyo peppers. CONCLUSIONS: The viability of NIRS for measuring dry matter content and soluble solid contents in situ, using a new-generation NIRS sensor, was demonstrated. However, the high water content, the irregular shape of the fruit, and the fact that it is hollow inside all point to the need for using larger samples sets so as to increase the robustness of the models obtained. © 2019 Society of Chemical Industry.

Safety and quality issues in summer squashes using handheld portable NIRS sensors for real-time decision making and for on-vine monitoring.

BACKGROUND: Portable handheld near infrared spectroscopy (NIRS) instruments currently present enormous advantages in terms of size, weight, and robustness. They also provide fast, precise information that can be obtained in situ, and they represent a viable option for controlling vegetable safety and quality during the growth period. The aim of this research was to evaluate three handheld portable NIRS instruments for in situ and real-time analysis of intact summer squashes. Traditional methods were used to analyze 221 summer squashes, and this work was used to develop calibration models for morphological, safety, and quality parameters. The longitudinal distribution of nitrate content in summer squashes weighing over 400 g was also studied, and the evolution of this parameter during the harvest period was tracked to determine which summer squashes and which zones of the vegetables (peduncle, equatorial, or stylar) could be earmarked for baby-food production. RESULTS: The robustness of the calibration models confirmed the expectations raised by NIRS technology for morphological, safety, and quality control of individual summer squashes, and the models developed with the MicroNIR-1700 instrument were those that provided more accuracy and precision, being the peduncle zone the part with higher nitrate content. CONCLUSIONS: It is in the peduncle zone, therefore, where measurements of this parameter must be carried out to decide on the destination of the harvested product. Summer squashes picked at the end of the harvest are those that must be used for baby-food production. © 2019 Society of Chemical Industry.

A Low-Cost IoT-Based System to Monitor the Location of a Whole Herd.

Animal location technologies have evolved considerably in the last 60 years. Nowadays, animal tracking solutions based on global positioning systems (GPS) are commercially available. However, existing devices have several constraints, mostly related to wireless data transmission and financial cost, which make impractical the monitorization of all the animals in a herd. The main objective of this work is to develop a low-cost solution to enable the monitorization of a whole herd. An IoT-based system, which requires some animals of the herd being fitted with GPS collars connected to a Sigfox network and the rest with low-cost Bluetooth tags, has been developed. Its performance has been tested in two commercial farms, raising sheep and beef cattle, through the monitorization of 50 females in each case. Several collar/tag ratios, which define the cost per animal of the solution, have been simulated. Results demonstrate that a low collar/tag ratio enable the monitorization of a whole sheep herd. A larger ratio is needed for beef cows because of their grazing behavior. Nevertheless, the optimal ratio depends on the purpose of location data. Large variability has been observed for the number of hourly and daily messages from collars and tags. The system effectiveness for the monitorization of all the animals in a herd has been certainly proved.

LOCAL regression applied to a citrus multispecies library to assess chemical quality parameters using near infrared spectroscopy.

The non-destructive on-tree measurement of the chemical quality attributes of fruits belonging to the Citrus genus using rapid spectral sensors is of vital interest to citrus growers, allowing them to carry out a selective harvest of any species of Citrus fruit. With this objective, the viability of using of a handheld portable near infrared spectroscopy (NIRS) instrument to predict soluble solid content (SSC), pH, titratable acidity (TA), maturity index and BrimA, in order to measure the optimum harvest time in a group made up of 608 samples belonging to the Citrus genus (378 oranges and 230 mandarins) was evaluated. For each of the parameters analysed, both non-linear regression (LOCAL algorithm) and linear regression (Modified Partial Least Squares, MPLS) strategies were designed and compared. The use of the LOCAL algorithm in the sample group of oranges and mandarins for all the parameters analysed allowed to obtain more robust models than those obtained with MPLS regression, and it could also be extended more easily when routinely applied. The results confirm that NIRS technology combined with non-linear regression strategies such as the LOCAL algorithm can indeed respond to the needs of the Citrus growers and help them to set the optimum harvest time, in this case of oranges and mandarins, by predicting the chemical quality parameters in situ.

Pre-harvest screening on-vine of spinach quality and safety using NIRS technology.

The study sought to perform a non-destructive and in-situ quality evaluation of spinach plants using near infrared (NIR) spectroscopy in order to establish its suitability for different uses once harvested. Modified partial least square (MPLS) regression models using NIR spectra of intact spinach leaves were developed for nitrate, ascorbic acid and soluble solid contents. The residual predictive deviation (RPD) values were 1.29, 1.21 and 2.54 for nitrate, ascorbic acid and soluble solid contents, respectively. Later, this predictive capacity increased for nitrate content (RPDcv = 1.63) when new models were developed, taking into account the influence on the robustness of the model exercised by the simultaneity between the NIR and laboratory analyses. Subsequently, using partial least squares discriminant analysis (PLS-DA), the ability of NIRS technology to classify spinach as a function of nitrate content was tested. PLS-DA yielded percentages of correctly classified samples ranging from 73.08-76.92% for the class 'spinach able to be used fresh' to 85.71-73.08% for the class 'preserved, deep-frozen or frozen spinach, both for unbalanced and balanced models respectively, based on NH signal associated with proteins. Overall, the data supports the capability of NIR spectroscopy to establish the final destination of the production of spinach analysed on the plant, as a screening tool for important safety and quality parameters.

Rapid, simultaneous, and in situ authentication and quality assessment of intact bell peppers using near-infrared spectroscopy technology.

BACKGROUND: The ability of near-infrared (NIR) spectroscopy to authenticate individual bell peppers as a function of the growing system (outdoor or greenhouse) was tested using partial least squares discriminant analysis. Bell peppers grown outdoors (130 samples) or in a greenhouse (264 samples) during the 2015 and 2016 seasons were selected for this purpose and analysed using a portable, handheld, microelectromechanical system (MEMS) instrument MicroPhazir (spectral range 1600-2400 nm), working in reflectance. Subsequently, the potential of NIR spectroscopy as a non-destructive sensor for in situ quality (dry matter and soluble solid content) measurements, was investigated. RESULTS: The models correctly classified 89.73% and 88.00% of the samples by growing system, when trained with unbalanced and balanced sets respectively, mainly due to the differences in physical-chemical attributes between bell peppers cultivated in the two growing systems. Separate classification models for bell peppers grouped by ripeness (judged by the colour), allowed the classification of 88.28-91.37% of the samples correctly. The standard error of cross-validation values for the quantitative models were 0.66% fresh weight and 0.75 °Brix for dry matter and soluble solid content, respectively. CONCLUSIONS: The results showed that NIR spectroscopy can be used successfully for predicting the growing systems used in bell pepper production, which is of particular value to guarantee the authentication of outdoor-grown peppers. Additionally, the results showed that NIR spectroscopy can be used simultaneously as a rapid preliminary screening technique to measure quality. © 2018 Society of Chemical Industry.

Evolution of Frying Oil Quality Using Fourier Transform Near-Infrared (FT-NIR) Spectroscopy.

This study assesses the capacity of a Fourier transform near-infrared (FT-NIR) spectrometer operating in the range 4500-12 000 cm-1 (833.33-2222.22 nm) to provide quantitative predictions for the parameters of acidity (AV), p-anisidine (pAV), total polar materials (TPM), peroxide value (PV), and oxidative stability index (OSI). 562 samples of frying oil were analyzed from 14 distinct types of oil. The calibrations obtained accounted for 96%, 95%, 99%, 92%, and 91% of the AV, pAV, TPM, PV, and OSI variations in the study set and the similarity between the standard error of laboratory (RMSEP) values and the reference method errors (RMSEL), enabling the authors to conclude that NIR technology has the capacity to replace traditional methods in thermo-oxidative degradation studies in frying oils.

Long-Length Fiber Optic Near-Infrared (NIR) Spectroscopy Probes for On-Line Quality Control of Processed Land Animal Proteins.

This research was conducted using a spectral database comprising 346 samples of processed animal proteins (PAPs) with a range of compositions, analyzed using a Fourier transform near-infrared spectroscopy multichannel instrument (Matrix-F, Bruker Optics) coupled to a 100 m fiber optic cable. Using both its static and dynamic operating modes (on a conveyor belt), simulating the movement of the product in the plant, the predictive capabilities of both modes of analysis were assessed and compared, for the purposes of predicting moisture, protein, and ashes. The results show that both exhibit highly similar degrees of precision and accuracy for predicting these parameters. This research provides a foundation of scientific-technical knowledge, hitherto unknown, regarding the "on-line" incorporation of an instrument (equipped with a 100 m fiber optic cable) into a processing plant of by-products of animal origin.

Fast, Low-Cost and Non-Destructive Physico-Chemical Analysis of Virgin Olive Oils Using Near-Infrared Reflectance Spectroscopy.

Near-Infrared (NIR) Spectroscopy was used for the non-destructive assessment of physico-chemical quality parameters in olive oil. At the same time, the influence of the sample presentation mode (spinning versus static cup) was evaluated using two spectrophotometers with similar optical characteristics. A total of 478 olive oil samples were used to develop calibration models, testing various spectral signal pre-treatments. The models obtained by applying MPLS regression to spectroscopic data yielded promising results for olive oil quality measurements, particularly for acidity, the peroxide index and alkyl and ethyl ester content. The results obtained indicate that this non-invasive technology can be used successfully by the olive oil sector to categorize olive oils, to detect potential fraud and to provide consumers with more reliable information. Although both sample presentation modes yielded comparable results, equations constructed with samples scanned using the spinning mode provided greater predictive capacity.