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.

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.

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.

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.

Near-Infrared Spectroscopy and Geostatistical Analysis for Modeling Spatial Distribution of Analytical Constituents in Bulk Animal By-Product Protein Meals.

Control and inspection operations within the context of safety and quality assessment of bulk foods and feeds are not only of particular importance, they are also demanding challenges, given the complexity of food/feed production systems and the variability of product properties. Existing methodologies have a variety of limitations, such as high costs of implementation per sample or shortcomings in early detection of potential threats for human/animal health or quality deviations. Therefore, new proposals are required for the analysis of raw materials in situ in a more efficient and cost-effective manner. For this purpose, a pilot laboratory study was performed on a set of bulk lots of animal by-product protein meals to introduce and test an approach based on near-infrared (NIR) spectroscopy and geostatistical analysis. Spectral data, provided by a fiber optic probe connected to a Fourier transform (FT) NIR spectrometer, were used to predict moisture and crude protein content at each sampling point. Variographic analysis was carried out for spatial structure characterization, while ordinary Kriging achieved continuous maps for those parameters. The results indicated that the methodology could be a first approximation to an approach that, properly complemented with the Theory of Sampling and supported by experimental validation in real-life conditions, would enhance efficiency and the decision-making process regarding safety and adulteration issues.

Evaluation of local approaches to obtain accurate near-infrared (NIR) equations for prediction of ingredient composition of compound feeds.

This research work investigated new methods to improve the accuracy of intact feed calibrations for the near-infrared (NIR) prediction of the ingredient composition. When NIR reflection spectroscopy, together with linear models, was used for the prediction of the ingredient composition, the results were not always acceptable. Therefore, other methods have been investigated. Three different local methods (comparison analysis using restructured near-infrared and constituent data [CARNAC]), locally weighed regression [LWR], and LOCAL) were applied to a large (N = 20 320) and heterogeneous population of non-milled feed compounds for the NIR prediction of the inclusion percentage of wheat and sunflower meal, as representative of two different classes of ingredients. Compared with partial least-squares regression, results showed considerable reductions of standard error of prediction values for all methods and ingredients: reductions of 59, 47, and 50% with CARNAC, LWR, and LOCAL, respectively, for wheat, and reductions of 49, 45, and 43% with CARNAC, LWR, and LOCAL, respectively, for sunflower meal. These results are a valuable achievement in coping with legislation and manufacture requirements concerning the labeling of intact feedstuffs.

Pixel selection for near-infrared chemical imaging (NIR-CI) discrimination between fish and terrestrial animal species in animal protein by-product meals.

This paper proposes a method based on near-infrared hyperspectral imaging for discriminating between terrestrial and fish species in animal protein by-products used in livestock feed. Four algorithms (Mahalanobis distance, Kennard-Stone, spatial interpolation, and binning) were compared in order to select an appropriate subset of pixels for further partial least squares discriminant analysis (PLS-DA). The method was applied to a set of 50 terrestrial and 40 fish meals analyzed in the 1000-1700 nm range. Models were then tested using an external validation set comprising 45 samples (25 fish and 20 terrestrial). The PLS-DA models obtained using the four subset-selection algorithms yielded a classification accuracy of 99.80%, 99.79%, 99.85%, and 99.61%, respectively. The results represent a first step for the analysis of mixtures of species and suggest that NIR-CI, providing valuable information on the origin of animal components in processed animal proteins, is a promising method that could be used as part of the EU feed control program aimed at eradicating and preventing bovine spongiform encephalopathy (BSE) and related diseases.

Measurement of pesticide residues in peppers by near-infrared reflectance spectroscopy.

BACKGROUND: Peppers are a frequent object of food safety alerts in various member states of the European Union owing to the presence in some batches of unauthorised pesticide residues. This study assessed the viability of near-infrared reflectance spectroscopy (NIRS) for the measurement of pesticide residues in peppers. Commercially available spectrophotometers using different sample-presentation methods were evaluated for this purpose: a diode-array spectrometer for intact raw peppers and two scanning monochromators fitted with different sample-presentation accessories (transport and spinning modules) for crushed peppers and for dry extract system for infrared analysis (DESIR), respectively. RESULTS: Models developed using partial least squares-discriminant analysis (PLS2-DA) correctly classified between 62 and 68% of samples by presence/absence of pesticides, depending on the instrument used. At model validation, the highest percentage of correctly classified samples-75 and 82% for pesticide-free and pesticide-containing samples respectively-were obtained for intact peppers using the diode-array spectrometer. CONCLUSION: The results obtained confirmed that NIRS technology may be used to provide swift, non-destructive preliminary screening for pesticide residues; suspect samples may then be analysed by other confirmatory analytical methods.

A methodology based on NIR-microscopy for the detection of animal protein by-products.

This study develops a methodology based on NIR-microscopy analysis and chemometric tools for the detection of animal protein by-products in mixtures, such as compound feeds and mixtures of ingredients, using a library of animal meal by-products only. The proposed methodology is a two-step strategy which worked better than the SIMCA approach it was compared with. In the first step, animal particles are identified using one of two methods, a global or a local distance measure. In the second, K-nearest-neighbours (KNN) is used to discriminate between terrestrial and fish particles. The models were developed using a training set comprising 11,727 spectra of pure terrestrial meals and 5843 of fish meals. KNN using second derivative spectra and five neighbours correctly classifies 98.5% of these samples under cross-validation. The procedure was validated using two external datasets, one made up of mixtures of species (fish and bovine), and a second of commercial compound feeds. The results obtained confirm that the procedure is able to reliably detect the presence of animal meals, although further work would be needed to develop it into an accurate quantitative method.

Use of near-infrared reflectance spectroscopy for shelf-life discrimination of green asparagus stored in a cool room under controlled atmosphere.

This study sought to evaluate the ability of near-infrared reflectance spectroscopy (NIRS) to classify intact green asparagus, in refrigerated storage under controlled atmosphere, by storage time and post-harvest treatments applied. A total of 468 green asparagus (Asparagus officinalis, L., cultivar UC-157) were sampled after 7, 14, 21 and 28 days of refrigerated storage (2 degrees C, 95% R.H.) under three controlled atmosphere (CA) treatments: air (21 kPa O(2)+0.3 kPa CO(2)), CA(1) (5 kPa O(2)+5 kPa CO(2)) and CA(2) (10 kPa O(2)+10kPa CO(2)). Two commercially available spectrophotometers were evaluated for this purpose: a scanning monochromator (SM) of 400-2500 nm and a combination of diode array and scanning monochromator (DASM) of 350-2500 nm. Models developed using partial least squares 2-discriminant analysis (PLS2-DA) correctly classified between 81-100% of samples by post-harvest storage time, depending on the instrument used. Using similar models, the DASM instrument correctly classified 85% of samples by post-harvest treatment, compared with 72% using the SM. These results confirmed that NIR spectroscopy, coupled with the use of chemometric techniques, provides a reliable, accurate method of predicting the shelf-life of asparagus under different storage conditions and as a function of post-harvest treatment applied; the method can be readily applied at industrial level.

Taking NIR calibrations of feed compounds from the laboratory to the process: calibration transfer between predispersive and postdispersive instruments.

In the context of current demands in the animal feed industry for controls and analyses, the use of instruments that may be applied on the process line has acquired a significant interest. A key aspect is that the calibrations developed for quality control with instruments sited in the laboratory (at-line) must be transferred to instruments that will be used in the plant itself (online). This study evaluates the standardization and the calibration transfer between a grating monochromator instrument (predispersive) designed for laboratory analysis and a diode array instrument (postdispersive) more adapted to process conditions. Two procedures that correct differences between spectra of two instruments were tested: the patented algorithm by Shenk and Westerhaus and piecewise direct standardization (PDS). Although results were slightly better with PDS, both methods achieved good spectral matching between the two instruments, with levels of repeatability similar to that of the grating instrument itself. The calibration transfer was evaluated in terms of the standard error of prediction (SEP), which was considerably reduced after standardization. However, final calibration models to be used in the diode array instrument must contain spectra from both types of instruments to give acceptable prediction accuracy.

Feasibility of diode-array instruments to carry near-infrared spectroscopy from laboratory to feed process control.

Near-infrared calibrations were developed for the instantaneous prediction of the chemical and ingredient composition of intact compound feeds. Two rather different instruments were compared (diode array vs grating monochromator). The grating monochromator was used in a static mode in the laboratory, whereas the diode-array instrumentbetter adapted to online analysiswas placed on a conveyor belt to simulate measurements at a feed mill plant. Modified partial least squares (MPLS) equations were developed using the same set of samples analyzed in the two instruments. Sample set 1 ( N = 398) was used to predict crude protein (CP) and crude fiber (CF), while sample set 2 ( N = 393) was used for the prediction of one macroingredient (sunflower meal, SFM) and one microingredient (mineral-vitamin premix, MVP). The standard error of cross-validation (SECV) and the coefficient of determination (R2) values for CF were better using the monochromator instrument. However, results obtained for CP, SFM, and MVP using the samples analyzed in the diode-array instrument showed similar or even greater accuracy than those obtained using samples analyzed in the grating monochromator. The excellent predictive ability [R2> 0.95; RPD (ratio of standard deviation to SECV) > 3] obtained for CP, CF, and SFM opens the way for the online use of NIRS diode-array instruments for surveillance and monitoring in the manufacture, processing, and marketing of compound feeds. R2, RPD, and SECV values for MVP showed similar performance for both instruments. Although RPD values did not reach the minimum recommended for quantitative analysis, results are encouraging for an ingredient present in feed compounds in such very low amounts.

Nondestructive determination of total soluble solid content and firmness in plums using near-infrared reflectance spectroscopy.

The fruit industry requires rapid, economical, and nondestructive methods for classifying fruit by internal quality, which can be built into the processing line. Total soluble solid content and firmness are the two indicators of plum internal quality that most affect consumer acceptance. These parameters are routinely evaluated using methods which involve destruction of the fruit; as a result, only control batches can be analyzed. The development of nondestructive analytical methods would enable the quality control of individual fruits. Near-IR spectroscopy (NIRS) was used to assess total soluble solid content (SSC, degrees Brix) and firmness (N) in intact plums. A total of 720 plums (Prunus salicina L. cv. 'African Pride', 'Black Diamond', 'Fortune', 'Laetitia', 'Larry Anne', 'Late Royal', 'Prime Time', 'Sapphire', and 'Songold') were used to obtain calibration models based on reference data and near-IR spectral data. Standard errors of cross-validation (SECV) and coefficients of determination for cross-validation (r(2)) were (0.77 degrees Brix; 0.83) for total soluble solids content and (2.54 N; 0.52) for firmness. Results suggest that NIRS technology enables fruit to be classified in terms of total soluble solid content and firmness, thus allowing increased sampling of each production batch and ensuring a given quality with greater precision and accuracy.

Near-infrared reflectance spectroscopy for predicting amino acids content in intact processed animal proteins.

Near-infrared calibrations were developed for the instantaneous prediction of amino acids composition of processed animal proteins (PAPs). Two sample presentation modes were compared (ground vs intact) for demonstrating the viability of the analysis in the intact form, avoiding the need for milling. Modified partial least-squares (MPLS) equations for the prediction of amino acids in PAPs were developed using the same set of samples (N = 92 PAPs) analyzed in ground and intact form and in three cups differing in the optical window size. The standard error for cross validation (SECV) and the coefficient of determination (1-VR) values yielded with the calibrations developed using the samples analyzed in the intact form showed similar or even better accuracy than those obtained with finely ground samples. The excellent predictive ability (1-VR > 0.90; CV < 3.0%) obtained for the prediction of amino acids in intact processed animal proteins opens an enormous expectative for the on-line implementation of NIRS technology in the processing and marketing of these important protein feed ingredients, alleviating the costs and time associated with the routine quality controls.

Parent and harvest year effects on near-infrared reflectance spectroscopic analysis of olive (Olea europaea L.) fruit traits.

The influence of parent and harvest year on the determination of oil, moisture, oleic acid, and linoleic acid contents in intact olive fruit was studied by near-infrared spectroscopy (NIRS). Spectral data from 400 to 1700 nm were recorded on 437 fruit samples collected in 1996 and 1997 from seedling plants derived from three different female parents. Partial least squares models were developed using samples for each year and for each female parent separately and were validated against the other groups. Calibration models were accurate enough to predict all constituents in new samples from a different female parent but were not transferable across years. However, a calibration equation of sufficient accuracy was obtained from the combined data set (r values of 0.94, 0.93, 0.84, and 0.88 and RMSECV values of 1.33, 1.88, 4.73, and 2.91 for oil, moisture, oleic acid, and linoleic acid contents, respectively). These results demonstrate the utility of NIRS as a selection tool in olive breeding programs.