Remote Reflectance Fibre-optic Probe Research Articles

The determination of fatty acids in cheeses of variable composition (cow, ewe's, and goat) by means of near infrared spectroscopy

The fat composition is one of the factors which has the greatest influence on cheese. The fatty acids present in the same influence sensory parameters such as color, texture, and flavor (rancid and pungent). They likewise influence the nutritional composition of cheese as different fatty acids have beneficial or harmful effects on human health. On the other hand, the determination of the fatty acids present in cheese has been put forward as a useful tool for distinguishing the various cheeses according to the milk used in their production. Finding a tool which allows the determination of the fatty acids present in cheese in a rapid and non destructive manner is of great interest to the cheese industry. In this study we examine the use of Near-Infrared Spectroscopy (NIRS) technology in the determination of 19 fatty acids in cheese from C8:0 to C20:0 including ∑SFA and ∑UFA. Cheeses were made with known and varying percentages of cow, ewe's, and goat milk (112 samples) and ripening controls were carried out for 6 months. Two ways of recording the spectra are compared, one using a remote reflectance fiber-optic probe on a slice of cheese and another using the fatty extracts obtained from the same cheeses and recorded with cam-lock cells. The regression method used is MPLS. The results obtained reveal that it is possible to predict the fatty acid composition of cheese by means of the use of NIRS, irrespective of the method used to record it. Furthermore, the results obtained in the validation of the method used indicate that the equations obtained allow their application to unknown cheese samples.

Flavonoid and Antioxidant Capacity of Propolis Prediction Using Near Infrared Spectroscopy

The use of propolis as a dietary supplement or as an ingredient in different food products is increasing, due to its antioxidant and bactericidal properties. These nutritional properties directly depend on its phenolic composition. For this reason, this study analysed the total contents of flavones and flavonols, flavanones and dihydroflavonols, and the antioxidant capacity by using the methods of ABTS and linoleic acid/β-carotene in 99 samples of propolis from Spain and Chile. A rapid method was developed for quantifying these parameters in raw propolis using near infrared (NIR) spectroscopy with a remote reflectance fibre-optic probe applied directly to the ground-up sample. The models developed allow for the determination of the total flavones and flavonols (0–183 mg quercetin/g propolis and 0–72 mg rutin/g propolis), of the total flavanones and dihydroflavonols (9–109 mg pinocembrin/g propolis extract), and of its antioxidant capacity by the ABTS method based on the reduction of the 2.2-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) radical cation(0–3212.6 nmol Trolox/mg of propolis) and of linoleic acid/β-carotene (22–86% inhibition). The NIR spectroscopy models were applied in external validation to different samples of the calibration group, which led to the conclusion that the methods developed provide significantly identical data to the initial chemical data of reference.

The potential of near infrared spectroscopy for determining the phenolic, antioxidant, color and bactericide characteristics of raw propolis

The phenolic composition (total phenols and flavonoids), antioxidant capacity measured using the Total Equivalent Antioxidant Capacity (TEAC) and the 1,1-diphenyl-2-picrylhydrazyl (DPPH) scavenging % methods, color (L* a*b*) and biocide properties of 53 propolis samples from Chile and Spain (Galicia and Castilla y León) were analyzed using methods of reference. Study of the correlations between the parameters examined revealed a significant correlation between the phenolic composition, the antioxidant activity and the color, the yellower and paler the color the lower the phenolic content and the antioxidant capacity. Moreover, there was a significant correlation between the flavonoid content and the bactericide properties. We studied the use of near infrared spectroscopy technology and of a remote reflectance fiber-optic probe, to predict these properties was studied. Calibration statistical descriptors on crushed propolis were excellent for total polyphenols, total flavonoids, the TEAC, the DPPH scavenging %, L*, a*, b*, and bactericide properties similar to beta-lactams and macrolides. The model generated permitted the determination of these variables in unknown samples of propolis.

Pesticide residues in propolis from Spain and Chile. An approach using near infrared spectroscopy

This study analyses the presence of pesticides in 106 samples of raw propolis from Spain and Chile. The components detected and quantified using gas chromatography mass spectrometry were as follows: triamidefon present in 70.1% of the samples with concentrations between 0.35 and 42.17mg/kg, and dicofol detected in 7.5% of the samples with concentrations between 0.39 and 2.83mg/kg. Diclorofluanid, procymidone, folpet, propham and metazachlor were also detected but only in a small number of samples. Near infrared spectroscopy with a remote reflectance fibre-optic probe was used to detect samples contaminated with triadimefon via partial least squares discriminant, with a R2 value of 0.71. In addition, the presence of the pesticide triadimefon in propolis was quantified using the modified partial least squares (MPLS) regression method. The calibration results showed a coefficient of determination (R2) of 0.81, a root mean square error (RMSE) of 0.36, and a ratio of performance to deviation (RPD) of 2.5.

Antioxidant capacity of different cheeses: Affecting factors and prediction by near infrared spectroscopy

In this study, we analyzed antioxidant capacity of 224 cheese samples prepared using 16 varied mixtures of milk from cows, ewes, and goats, in 2 manufacturing seasons (winter and summer), and over 6mo of ripening. Antioxidant capacity was evaluated using the spectrophotometric 2,2-azinobis(3-ethylenebenzothiazoline-6-sulfonic acid) (ABTS) method. Total antioxidant capacity was significantly correlated with season of manufacturing and time of ripening but not with animal species providing the milk. Moreover, statistically significant correlations between the total antioxidant capacity and retinol (r=0.399), fat percentage (r=0.308), protein percentage (r=0.366), K (r=0.385), Mg (r=0.312), Na (r=0.432), and P (0.272) were observed. We evaluated the use of near infrared spectroscopy technology, together with the use of a remote reflectance fiber-optic probe, to predict the antioxidant capacity of cheese samples. The model generated allowed us to predict antioxidant capacity in unknown cheeses of different compositions and ripening times.

Application of VIS/NIR spectroscopy for estimating chemical, physical and mechanical properties of cork stoppers

The aim of this study was to evaluate the potential of visible and near infrared spectroscopy (Vis/NIRS) in predicting the chemical, physical and mechanical behavior of single-piece natural corks stoppers used for sealing wine bottles. Two training sets of 90 and 150 cork stoppers were used to obtain four spectra per sample in different positions: two of the stopper bases (transversal section) and two of the stopper sides (tangential section and radial section). The samples were scanned in the range of 400–2,500 nm using a Foss-NIRSystems 6500 SY II spectrophotometer equipped with a remote reflectance fiber-optic probe. On each training set, two-thirds of the samples were used to develop modified partial least square (MPLS) calibration equations, and the remaining one-third of the sample for the external validation of these MPLS equations. The best equations were obtained for the transversal section, which is the recommended one when applying Vis/NIRS technology to cork. The best results for the chemical composition were obtained for waxes and total polyphenols, showing coefficient of determination of the cross validation (r cv 2 ) values of 0.64 and 0.56 and coefficient of determination of the external validation (r EV 2 ) values of 0.53 and 0.55, respectively. The best equation for the physical and mechanical parameters was obtained for moisture content (r cv 2 = 0.86 and r EV 2 = 0.85), with somewhat lower results for density, compression force and extraction force (r cv 2 = 0.66, 0.72, 0.52 and r EV 2 = 0.52, 0.49, 0.51, respectively). The SECV (standard error of cross validation) and SEP (standard error of external validation) were similar for all the physical and mechanical parameters, thus confirming the robustness of the equations. MPLS model for moisture content fulfills the requirements for screening (RPD >2.5), but MPLS models obtained for waxes, total polyphenols, density, compression force and extraction force are not good enough for routine analysis or quality control. The results obtained from the MPLS models based on Vis/NIRS technology would permit the continuous quality control of humidity in the production line as well as obtaining information about certain chemical components (extractives contents) and some physical and mechanical parameters (density, extraction force and compression force).

Chemical characteristics and mineral composition of quinoa by near‐infrared spectroscopy

Quinoa (Chenopodium quinoa Willd.) has been traditionally used as a foodstuff in the Andes and it has gained increasing interest in recent years owing to its high nutritional value. The potential of near-infrared spectroscopy (NIRS) for estimating chemical compounds in quinoa was studied because of the possibility of conducting direct measurement without prior sample treatment. The use of NIRS technology with a remote reflectance fiber-optic probe for the analysis of major compounds and mineral composition of 48 quinoa samples was studied. The calibration of the NIRS instrument was conducted using modified partial least squares (MPLS) analysis. This allowed the determination of protein (16.0-20.2 g 100g⁻¹), crude fiber (1.8-3.1 g 100g⁻¹), fat (4.4-7.5 g 100g⁻¹), calcium (298.8-1164.5 mg kg⁻¹), iron (0-948.5 mg kg⁻¹) and phosphorus (2735.0-4543.3 mg kg⁻¹). The correlation coefficients (RSQ) were 0.83 for protein, 0.73 for crude fiber, 0.93 for fat; 0.60 for calcium; 0.76 for iron and 0.75 for phosphorus. The robustness of the equations obtained was verified by external validation on unknown quinoa samples. NIRS with fiber-optic probe provides an alternative for the determination of chemical compounds of quinoa, faster and at lower cost, with results comparable with chemical methods.

Determination of the Mineral Composition (Ca, P, Mg, K, Na) in Cheeses (Cow's, Ewe's and Goat's) with Different Ripening Times using Near Infrared Spectroscopy (NIRs) with a Fibre-Optic Probe

In the present work we study the use of NIRS technology together with the use of a remote reflectance fibre-optic probe (with a 5 cm × 5 cm quartz window) for the determination of mineral composition (Ca, P, Mg, K, Na) in 227 samples of cheeses elaborated with 16 varying mixtures of milk from cows, ewes and goats with different ripening times over 6 months. The NIR spectra were recorded in reflectance mode, applying the fibre-optic probe directly onto the cheese simple, with no simple preparation or manipulation.

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

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

The application of near infrared spectroscopy technology and a remote reflectance fibre-optic probe for the determination of peptides in cheeses (cow’s, ewe’s and goat’s) with different ripening times

The use of near infrared spectroscopy (NIRS) technology employing a remote reflectance fibre-optic probe (with a 5 cm × 5 cm quartz window) for the analysis of the hydrophilic (HI) and hydrophobic (HO) peptides and the ratio HO/HI was assayed . To do so, cheeses with known and varying percentages of cow’s, ewe’s and goat’s milk were elaborated (112 samples). Ripening controls were performed over 6 months, and the chemical data obtained by reversed-phase high performance liquid chromatography used as reference. The regression method employed was modified partial least squares (MPLS). The multiple correlation coefficients (RSQ) and prediction corrected standard errors (SEP (C)) obtained 0.879 and 1.83% for hydrophilic (HI) and 0.879 and 1.83% for hydrophobic (HO) peptides, respectively, and 0.890 and 0.03% for the ratio HO/HI. The method allows immediate control by direct application of the fibre-optic probe to the cheese without prior sample treatment or destruction.

Texture evaluation in cheeses by NIRS technology employing a fibre-optic probe

The use of NIRS (near infra-red spectroscopy) technology together with a remote-reflectance fibre-optic probe was studied for the analysis of ewe’s cheese texture. The cheeses were elaborated with milk from three breeds of sheep (Castellana, Churra and Assaf) with different somatic cell counts (SCC) (lower than 500,000 cells m L −1; between 1,000,000 and 1,500,000 cells m L −1, and more than 2,500,000 cells m L −1) and ripened for 12 months, thus 96 samples were obtained. At each ripening time NIRS spectra were recorded and instrumental texture were determined as Warner–Bratzler shear force using a TX-T2i plus. The regression method employed was modified partial least squares (MPLS). The calibration, with the multiple correlation coefficients (RSQ), 0.961 and standard error of prediction (SEP(C)) of 2.1 N, allows determining texture in the range 0.0–49.0 N. The ratio performance deviation (RPD) value obtained, 5.4, indicate that the NIRS equation obtained was applicable to unknown samples.

Determination of hydroxyproline in cured pork sausages and dry cured beef products by NIRS technology employing a fibre–optic probe

In the present work a study was made of the use of near infra-red spectroscopy (NIRS) technology with a remote reflectance fibre–optic probe for the analysis of hydroxyproline in cured pork sausages ( salchichón and chorizo) and dry cured beef ( cecina). The presence of the amino acid hydroxyproline was used as an analytical criterion to assess the amount of collagen from connective tissues versus lean meat (muscle). The fibre–optic probe was placed directly on the sample with no previous treatment or manipulation. The regression method employed was that of modified partial least squares (MPLS). The equation developed for a total of 80 samples ( salchichón, chorizo and cecina) allowed the determination of the hydroxyproline content within a range of 0–0.74%. The multiple correlation coefficient (RSQ) and standard error of prediction corrected (SEP(C)) obtained were respectively 0.772 and 0.05%. The predicted values of hydroxyproline in unknown samples using NIRS technology and applying the fibre–optic probe directly on the sample without previous treatment were comparable to those obtained using the chemical method.

Potential of near infrared spectroscopy for the analysis of mycotoxins applied to naturally contaminated red paprika found in the Spanish market

The potential of the near infrared spectroscopy (NIRS) technique for the analysis of red paprika for aflatoxin B 1, ochratoxin A and total aflatoxins is explored. As a reference, the results from a chromatographic method with fluorescence detection (HPLC-FD) following an immunoaffinity cleanup (IAC) were employed. For the NIRS measurement, a remote reflectance fibre-optic probe was applied directly onto the samples of paprika. There was no need for pre-treatment or manipulation of the sample. The modified partial least squares (MPLS) algorithm was employed as a regression method. The multiple correlation coefficients (RSQ) and the prediction corrected standard errors (SEP(C)) were respectively 0.955 and 0.2 μg kg −1, 0.853 and 2.3 μg kg −1, 0.938 and 0.3 μg kg −1 for aflatoxin B 1, ochratoxin A and total aflatoxins, respectively. The capacity for prediction of the developed model measured as ratio performance deviation (RPD) for aflatoxin B 1 (5.2), ochratoxin A (2.8) and total aflatoxins (4.4) indicate that NIRS technique using a fibre-optic probe offers an alternative for the determination of these three parameters in paprika, with an advantageously lower cost and higher speed as compared with the chemical method. Content of aflatoxin B 1 and total aflatoxins are the parameters currently employed by the food regulations to limit the levels of the four aflatoxins in many foodstuffs. In addition, aflatoxin B 1 itself is an excellent indicator for aflatoxins’ contamination since it is always the most abundant and toxic.

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

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

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

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

Determination of the percentage of milk (cow's, ewe's and goat's) in cheeses with different ripening times using near infrared spectroscopy technology and a remote reflectance fibre-optic probe

In the present work we studied the use of near infrared spectroscopy (NIRS) technology employing a remote reflectance fibre-optic probe (with a 5 cm × 5 cm quartz window) for the analysis of the percentage of milk (cow's, ewe's and goat's) used in the elaboration of cheeses with different ripening times. To do so, cheeses with known and varying percentages of cow's, ewe's and goat's milk were elaborated (112 samples with milk collected in winter and 112 samples with milk collected in summer) and used as reference material, and ripening controls were performed over 6 months. The method allows immediate control of the cheese without prior sample treatment or destruction by direct application of the fibre-optic probe to the sample. The regression method employed was modified partial least squares (MPLS). Of all the samples (224), 200 formed to so-called calibration set and the other 24 were used for external validation. The calibration results obtained using 200 samples of cheese allowed the percentage of cow's, ewe's and goat's milk to be measured. The multiple correlation coefficients (RSQ) and prediction corrected standard errors (SEP(C)) obtained were respectively, 0.834 and 11.6% for cow's milk; 0.871 and 9.8% for goat's milk; 0.880 and 10.6% for ewe's milk. The ratio performance deviation (RPD) values obtained indicate that the NIRS equations can be applied to unknown samples.

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

In the present work, we study the use of near infra-red spectroscopy (NIRS) technology together with a remote reflectance fibre-optic probe for determination of the major components in bee pollen. The method allows immediate control of the bee pollen without prior sample treatment or destruction through direct application of the fibre-optic probe to the sample. The regression method employed was modified partial least squares (MPLS). The calibration results obtained using 45 samples of bee pollen allowed the measurement of protein, moisture, ash, reducing sugars, and pH with multiple correlation coefficients (RSQ) and prediction corrected standard errors (SEPC) of 0.91, 0.56% for protein, of 0.78 and 0.49% for moisture; 0.92 and 0.049% for ash; 0.81 and 1.32 g of glucose/100 g of bee pollen; 0.84 and 0.15 for pH, respectively. The prediction capacity of the pattern was checked by applying it to samples of unknown pollen in external validation.

Use of NIRS technology with a remote reflectance fibre-optic probe for predicting mineral composition (Ca, K, P, Fe, Mn, Na, Zn), protein and moisture in alfalfa

In the present work we study the use of near-infrared spectroscopy (NIRS) technology together with a remote reflectance fibre-optic probe for the analysis of major (Ca, K, P) and minor (Fe, Mn, Na, Zn) elements, protein and moisture in alfalfa. The method allows immediate analysis of the alfalfa without prior sample treatment or destruction through direct application of the fibre-optic probe on ground samples in the case of the mineral composition and on-ground and compacted (baled) samples in the case of protein and humidity. The regression method employed was modified partial least-squares (MPLS). The calibration results obtained using samples of alfalfa allowed the determination of Ca, K, P, Fe, Mn, Na and Zn, with a standard error of prediction (SEP(C)) and a correlation coefficient (RSQ) expressed in mg/kg of alfalfa of 1.37x10(3) and 0.878 for Ca, 1.10x10(3) and 0.899 for K, 227 and 0.909 for P, 103 and 0.948 for Fe, 5.1 and 0.843 for Mn, 86.2 and 0.979 for Na, and of 1.9 and 0.853 for Zn, respectively. The SEP(C) and RSQ values (in %) for protein and moisture in ground samples were 0.548 and 0.871 and 0.150 and 0.981, respectively; while in the compacted samples they were 0.564 and 0.826 and 0.262 and 0.935, respectively. The prediction capacity of the model and the robustness of the method were checked in the external validation in alfalfa samples of unknown composition, and the results confirmed the suitability of the method.

Near-infrared spectroscopy (NIRS) reflectance technology for the determination of tocopherols in alfalfa

The vitamin E (alpha- and (beta+gamma)-tocopherol) contents present in alfalfa (fresh or dehydrated) were analysed using near-infrared spectroscopy (NIRS) technology together with a remote reflectance fibre-optic probe. The range of vitamin E was 0.55-5.16 mg/100 g for alpha-tocopherol and 0.07-0.48 for (beta+gamma)-tocopherol. The regression method employed was modified partial least squares (MPLS). The equations developed using the fibre-optic probe for 69 samples of alfalfa (dehydrated and fresh) to determine the content of vitamin E in feeds had multiple correlation coefficients (RSQs) and prediction corrected standard errors (SEP (C)) of 0.946 and 0.321 mg/100 g for alpha-tocopherol and 0.956 and 0.022 mg/100 g for (beta+gamma)-tocopherol. The predicted values of vitamin E in feeds using NIRS technology applying the fibre-optic probe directly on the sample with neither previous treatment nor manipulation are comparable to those obtained using the chemical method, which included alkaline hydrolysis and hexane extraction of the vitamin from the unsaponifiable fraction before chromatographic determination.

Instantaneous determination of crude proteins, fat and fibre in animal feeds using near infrared reflectance spectroscopy technology and a remote reflectance fibre-optic probe

In the present study we address the development of a rapid technique – near infrared reflectance spectroscopy (NIRS) technology and the use of a remote reflectance crude fibre (CF)-optic probe (fitted with a 5 cm × 5 cm quartz window) – to perform instantaneous analysis of quantitative parameters such as the ether extract (DM), crude protein (CP) and crude fibre in animal feed and fodder. The technique does not involve the destruction or treatment of the sample because it is based on direct application of a fibre-optic probe on the feed (with different physical characteristics, such as commercialisation in the form of meal, tablets and granules) given to cattle, swine, sheep, birds and rabbits that is used in different stages of animal development—juvenile development, growth, fattening, etc. The regression method employed was modified partial least squares (MPLS). The calibration results using 72 samples permitted the determination of crude protein, the ether extract and crude fibre. This calibration is applicable within the ranges for crude protein of 5–495 g kg −1; the ether extract, 16–66 g kg −1; crude fibre, 23–111 g kg −1, with multiple correlation coefficients (RSQ) for crude protein, the ether extract and crude fibre of 0.991, 0.932 and 0.848, respectively. The standard errors of prediction corrected (SEPC) for the above parameters in g kg −1 were 12.5, 2.09 and 6.05, respectively. The robustness of the analytical method was confirmed by applying it to 10 samples for external validation. The results obtained indicate that NIRS with a fibre-optic probe can be used as a quality control method in animal feeds and fodder for the determination of crude protein, the ether extract and crude fibre.