Rapid determination of Swiss cheese composition by Fourier transform infrared/attenuated total reflectance spectroscopy

There is a need for rapid and simple techniques that can be used to predict the quality of cheese. The aim of this research was to develop a simple and rapid screening tool for monitoring Swiss cheese composition by using Fourier transform infrared spectroscopy. Twenty Swiss cheese samples from different manufacturers and degree of maturity were evaluated. Direct measurements of Swiss cheese slices (∼0.5g) were made using a MIRacle 3-reflection diamond attenuated total reflectance (ATR) accessory. Reference methods for moisture (vacuum oven), protein content (Kjeldahl), and fat (Babcock) were used. Calibration models were developed based on a cross-validated (leave-one-out approach) partial least squares regression. The information-rich infrared spectral range for Swiss cheese samples was from 3,000 to 2,800 cm⁻¹ and 1,800 to 900 cm⁻¹. The performance statistics for cross-validated models gave estimates for standard error of cross-validation of 0.45, 0.25, and 0.21% for moisture, protein, and fat respectively, and correlation coefficients r > 0.96. Furthermore, the ATR infrared protocol allowed for the classification of cheeses according to manufacturer and aging based on unique spectral information, especially of carbonyl groups, probably due to their distinctive lipid composition. Attenuated total reflectance infrared spectroscopy allowed for the rapid (∼3-min analysis time) and accurate analysis of the composition of Swiss cheese. This technique could contribute to the development of simple and rapid protocols for monitoring complex biochemical changes, and predicting the final quality of the cheese.     

Classification of Swiss cheese starter and adjunct cultures using Fourier transform infrared microspectroscopy

The acceptability of Swiss cheese largely depends on the flavor profile, and strain variations of cheese cultures will affect the final quality. Conventional biochemical methods to identify the cultures at the strain level are time-consuming and expensive, and require skilled labor. Our objective was to develop rapid classification methods of starter cultures at the strain level by using a combination of hydrophobic grid membrane filters and Fourier transform infrared (FTIR) spectroscopy. Forty-four pulsed-field gel electrophoresis-verified strains of starter and nonstarter cultures including Streptococcus thermophilus, Propionibacterium freudenreichii, and Lactobacillus spp. were analyzed. The strains were grown on their respective agar media, transferred to broth media, and incubated. Then, cultures were centrifuged and the pellets were resuspended in saline solution (10 μL). Aliquots (2 μL) of the suspended bacterial solution were placed onto a grid of the hydrophobic grid membrane filters, having 6 grids per each strain analyzed. The dried filters were read by FTIR microspectroscopy fitted with an attenuated total reflectance probe. Collected spectra were statistically analyzed by a soft independent modeling of class analogy (SIMCA) for pattern recognition. Classification models were developed for Streptococcus thermophilus, Propionibacterium freudenreichii, and Lactobacillus spp. strains. The models showed major discrimination in the spectral region from 1,200 to 900 cm⁻¹ associated with signals from phosphate-containing compounds and various polysaccharides in the cell wall. The developed method allowed for rapid classification of several Swiss cheese starter and nonstarter cultures at the strain level. This information provides a detailed overview of microbiological status, which would enable corrective measures to be taken early in the cheese making process, limiting production of inferior quality cheese and minimizing defects. This method could be an effective tool to identify and monitor activity of cheese and other dairy starter cultures.     

Quantification of short-chain free fatty acids in "Terrincho" ewe cheese: intravarietal comparison

Headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry was employed for the quantification of volatile free fatty acids (FFA) in "Terrincho" ewe cheese. Solid-phase microextraction quantitative analysis was feasible under equilibrium situations as long as the conditions of agitation and the adsorption time were held constant. An excellent linear relationship between the amount of the adsorbed analyte and its initial concentration in the sample matrix was obtained when an adequate amount of sample was chosen. Thus, quantification was possible if biases due to competition or linear range excesses were controlled. Solid-phase microextraction sampling was carried out at 65 degrees C, and a fiber coated with an 85-micro/m polyacrylate film was chosen. After equilibration at 65 degrees C for 40 min, the fiber was exposed to the headspace above the sample for 20 min and then inserted into the gas chromatograph. The evolution of the volatile FFA during Terrincho ewe cheese ripening was analyzed for a 60-d period. An overall increase in FFA contents was verified up to 30 d of ripening. Between 30 and 45 d most FFA did not suffer significant changes. All FFA increased significantly by the 60-d ripening period. The excessive lipolysis observed at 60 d of ripening may result in the presence of off-flavors. Principal component analysis performed for intravarietal comparison of volatile FFA composition of 19 Terrincho cheeses, analyzed at 30 ripening days, enabled discrimination between cheeses produced at five different dairy plants.     

Influence of starter culture ratios and warm room treatment on free fatty acid and amino acid in Swiss cheese

Quantification of water-soluble volatile free fatty acids (FFA) and free amino acids (FAA) was performed as a ripening index and an indirect measure of flavor development in Swiss-type cheeses. The objective of this research was to assess the effect of warm room treatment (WRT) and usage ratio of starter cultures, Streptococcus thermophilus and Lactobacillus helveticus vs. propionibacteria, on the concentration of FFA and FAA in pilot plant-scale Swiss cheese. A capillary gas chromatograph equipped with a flame ionization detector was used for the analysis of FFA in Swiss cheese. Free amino acids were analyzed by the Cd-ninhydrin method. Starter culture ratios did not affect development of FAA during the cheese ripening. However, duration of WRT had an effect on the concentration of FAA in the Swiss cheese. Free amino acids increased considerably during WRT. A continuous increase in FAA was shown during 70-d ripening time after WRT. The concentrations of C2:0 and C3:0 fatty acids were affected by starter culture ratios after 2-wk WRT, but these differences had mostly disappeared after 3-wk WRT. Similar concentrations of FFA and FAA reported in previous studies were developed in Swiss cheese with a 3-wk WRT and a 0.33:1 ratio of Streptococcus thermophilus and Lactobacillus helveticus to propionibacteria.     

Application of infrared microspectroscopy and multivariate analysis for monitoring the effect of adjunct cultures during Swiss cheese ripening

Improved cheese flavor has been attributed to the addition of adjunct cultures, which provide certain key enzymes for proteolysis and affect the dynamics of starter and nonstarter cultures. Infrared microspectroscopy provides unique fingerprint-like spectra for cheese samples and allows for rapid monitoring of cheese composition during ripening. The objective was to use infrared microspectroscopy and multivariate analysis to evaluate the effect of adjunct cultures on Swiss cheeses during ripening. Swiss cheeses, manufactured using a commercial starter culture combination and 1 of 3 adjunct Lactobacillus spp., were evaluated at d 1, 6, 30, 60, and 90 of ripening. Cheese samples (approximately 20 g) were powdered with liquid nitrogen and homogenized using water and organic solvents, and the water-soluble components were separated. A 3-μL aliquot of the extract was applied onto a reflective microscope slide, vacuum-dried, and analyzed by infrared microspectroscopy. The infrared spectra (900 to 1,800 cm⁻¹) produced specific absorption profiles that allowed for discrimination among different cheese samples. Cheeses manufactured with adjunct cultures showed more uniform and consistent spectral profiles, leading to the formation of tight clusters by pattern-recognition analysis (soft independent modeling of class analogy) as compared with cheeses with no adjuncts, which exhibited more spectral variability among replicated samples. In addition, the soft independent modeling of class analogy discriminating power indicated that cheeses were differentiated predominantly based on the band at 1,122 cm⁻¹, which was associated with S-O vibrations. The greatest changes in the chemical profile of each cheese occurred between d 6 and 30 of warm-room ripening. The band at 1,412 cm⁻¹, which was associated with acidic AA, had the greatest contribution to differentiation, indicating substantial changes in levels of proteolysis during warm-room ripening in addition to propionic acid, acetic acid, and eye formation. A high-throughput infrared microspectroscopy technique was developed that can further the understanding of biochemical changes occurring during the ripening process and provide insight into the role of adjunct nonstarter lactic acid bacteria on the complex process of flavor development in cheeses.     

Cheddar cheese classification based on flavor quality using a novel extraction method and Fourier transform infrared spectroscopy

Analysis of Cheddar cheese flavor using trained sensory and grading panels is expensive and time consuming. A rapid and simple solvent extraction procedure in combination with Fourier transform infrared spectroscopy was developed for classifying Cheddar cheese based on flavor quality. Fifteen Cheddar cheese samples from 2 commercial production plants were ground into powders using liquid nitrogen. The water-soluble compounds from the cheese powder, without interfering compounds such as fat and protein, were extracted using water, chloroform, and ethanol. Aliquots (10 μL) of the extract were placed on a zinc selenide crystal, vacuum dried, and scanned in the mid-infrared region (4,000 to 700 cm⁻¹). The infrared spectra were analyzed by soft independent modeling of class analogy (SIMCA) for pattern recognition. Sensory flavor quality of these cheeses was determined by trained quality assurance personnel in the production facilities. The SIMCA models provided 3-dimensional classification plots in which all the 15 cheese samples formed well-separated clusters. The orientation of the clusters in 3-dimensional space correlated well with their cheese flavor characteristics (fermented, unclean, low flavor, sour, good Cheddar, and so on). The discrimination of the samples in the SIMCA plot was mainly due to organic acids, fatty acids and their esters, and amino acids (1,450 to 1,350 and 1,200 to 990 cm⁻¹), which are known to contribute significantly to cheese flavor. The total analysis time, including the sample preparation time, was less than 20 min per sample. This technique can be a rapid, inexpensive, and simple tool to the cheese industry for predicting the flavor quality of cheese.     

A chemometric approach for the quantification of free fatty acids in cottonseed oil by Fourier transform infrared spectroscopy

The aim of this study was to quantify free fatty acid in cottonseed oil (Gossypium) variety by a chemometric approach using Fourier transform infrared spectroscopy. Calibration standards were prepared by gravimetrical mixing of oleic acid (0.1–40 g/100 g) in neutralized cottonseed oil containing <0.1% free fatty acids. Fourier transform infrared technique coupled with partial least square and principle component regression models were used to develop calibrations in the specific absorption region of carbonyl between 1690–1727 cm^?1. On the basis of regression coefficient and evaluated free fatty acids results with comparison to titration method, partial least square was found to be more accurate than principle component regression calibration model. All the analyzed cottonseed oil varieties showed high content of free fatty acids in the range of 17.1–38.5%. The results of the present study indicated that Fourier transform infrared method in combination with partial least square or principle component regression could be used as a greener alternative to the standard titration method.     

Identification of free fatty acids and some other volatile flavour compounds from Swiss cheese using on-line supercritical fluid extraction-gas chromatography

On-line supercritical CO₂ extraction - gas chromatography was applied to the isolation and identification of free fatty acids and other volatile compounds of young and ripe Swiss cheese (Emmental) produced in Finland. Extractions were carried out using a micro-cartridge at 40 °C temperature and 10 MPa pressure and the volatile fractions were analysed by DB-WAX column (polyethylene glycol phase) with flame ionization detection and mass spectrometry. The total time of analysis was less than 2.5 h. Acetic acid and propionic acid predominated over the C12–C18 acids, the longer chain fatty acids increasing in concentration during the ripening of the cheese. Due to the high proportions of fatty acids, further fractionation is required for analysis of the less abundant aroma compounds such as alcohols, carbonyls and lactones.     

基于傅里叶变换红外光谱法的红花药材质量分析

目的 建立傅里叶变换红外光谱法分析评价红花药材质量的分析方法。方法 采用傅立叶变换红外光谱法(Fourier transform infrared spectrometry, FTIR)对采集自新疆不同产地的20批红花药材进行测量, 建立指纹图谱; 运用聚类分析与主成分分析等统计学及化学模式识别技术, 对不同来源的红花药材进行红外光谱数据比较分析。结果 筛选出12个波数段, 形成了红花药材FTIR红外光谱指纹图谱共有模式; 精密度、稳定性和重复性实验结果表明, 共有峰波数的相对标准偏差(relative standard deviation, RSD)分别为0.00%~0.1%、0.00%~4.56%、0.32%~7.5%, 符合指纹图谱的要求; 20批样品相似度均大于95%, 大致可以分为3大类, 区域间差异较为明显。结论 本研究建立的方法简单、易行、快速, 不污染、样品耗量低, 为不同来源红花药材的鉴定、内在质量评价与质量控制提供了依据、奠定了基础。     

Classification of Pecorino cheeses produced in Italy according to their ripening time and manufacturing technique using Fourier transform infrared spectroscopy

Fourier-transform infrared spectroscopy, followed by linear discriminant analysis of the spectral data, was used to classify Italian Pecorino cheeses according to their ripening time and manufacturing technique. The Fourier transform infrared spectra of the cheeses were divided into 18 regions and the normalized absorbance peak areas within these regions were used as predictors. Linear discriminant analysis models were constructed to classify Pecorino cheeses according to different ripening stages (hard and semi-hard) or according to their manufacturing technique (fossa and nonfossa cheeses). An excellent resolution was achieved according to both ripening time and manufacturing technique. Also, a final linear discriminant analysis model considering the 3 categories (hard nonfossa, hard fossa, and semi-hard nonfossa) was constructed. A good resolution among the 3 categories was obtained.     

Application of Fourier transform infrared spectroscopy to legume seed flour analysis

The secondary structure of legume (Phaseolus vulgaris L. and Lens culinaris L.) proteins was investigated by studying the amide I infrared absorption band in whole seed flours, before and after dry heating and autoclaving thermal treatments. The analysis procedure, set up on 7S and different model proteins, shows that the content of β-sheet structures in lentil is higher than in common bean (47% vs. 32%). The dry heating does not appreciably affect secondary structures in lentil, while it causes a reduction of β-sheets (to 13%), an increase of aggregates, and the appearance of random coil structures in common bean. The autoclaving treatment produces high amounts of aggregates in both legumes. However, in lentil, random coil structures are lower than in common bean and some β-sheet structures are still detectable. These results indicate that multimeric heat-induced complexes of legume proteins have a high stability because of the high content in β-sheet structures, in particular in lentil, which may adversely affect protein utilization.     

反式脂肪酸红外光谱和气相色谱分析

用傅立叶转换红外光谱测定好丽友·派、乐思人造奶油和法国百吉福天天奶酪样品反式脂肪酸含量,分别为30．9%、13．2%和7．9%；费列罗榛果威化果仁巧克力样品未检测出反式脂肪酸含量。用Agilent 112-88A7HP-88毛细管柱分析样品中脂肪酸组成和含量,顺、反式脂肪酸分离良好,检出限为9．8μm/mL,线性γ值为0．9992,加标回收率为89．54%～102．85%,相对标准偏差(RSD)为0．92%～1．46%；测得好丽友·派、乐思人造奶油和法国百吉福天天奶酪样品中十八碳反式单烯酸含量分别为20．70%、8．72%和3．98%,约占总反式脂肪酸量50%～60%。     

Quantitative determination of fatty acid compositions in micro-encapsulated fish-oil supplements using Fourier transform infrared (FTIR) spectroscopy

The research describes a rapid method for the determination of fatty acid (FA) contents in a micro-encapsulated fish-oil (μEFO) supplement by using attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopic technique and partial least square regression (PLSR) analysis. Using the ATR-FTIR technique, the μEFO powder samples can be directly analysed without any pre-treatment required, and our developed PLSR strategic approach based on the acquired spectral data led to production of a good linear calibration with R(2)=0.99. In addition, the subsequent predictions acquired from an independent validation set for the target FA compositions (i.e., total oil, total omega-3 fatty acids, EPA and DHA) were highly accurate when compared to the actual values obtained from standard GC-based technique, with plots between predicted versus actual values resulting in excellent linear fitting (R(2)≥0.96) in all cases. The study therefore demonstrated not only the substantial advantage of the ATR-FTIR technique in terms of rapidness and cost effectiveness, but also its potential application as a rapid, potentially automated, online monitoring technique for the routine analysis of FA composition in industrial processes when used together with the multivariate data analysis modelling.     

Quantification of individual fatty acids in bovine milk by infrared spectroscopy and chemometrics: Understanding predictions of highly collinear reference variables

Predicting individual fatty acids (FA) in bovine milk from Fourier transform infrared (FT-IR) measurements is desirable. However, such predictions may rely on covariance structures among individual FA and total fat content. These covariance structures may change with factors such as breed and feed, among others. The aim of this study was to estimate how spectral variation associated with total fat content and breed contributes to predictions of individual FA. This study comprised 890 bovine milk samples from 2 breeds (455 Holstein and 435 Jersey). Holstein samples were collected from 20 Danish dairy herds from October to December 2009; Jersey samples were collected from 22 Danish dairy herds from February to April 2010. All samples were from conventional herds and taken while cows were housed. Moreover, in a spiking experiment, FA (C14:0, C16:0, and C18:1 cis-9) were added (spiked) to a background of commercial skim milk to determine whether signals specific to those individual FA could be obtained from the FT-IR measurements. This study demonstrated that variation associated with total fat content and breed was responsible for successful FT-IR–based predictions of FA in the raw milk samples. This was confirmed in the spiking experiment, which showed that signals specific to individual FA could not be identified in FT-IR measurements when several FA were present in the same mixture. Hence, predicted concentrations of individual FA in milk rely on covariance structures with total fat content rather than absorption bands directly associated with individual FA. If covariance structures between FA and total fat used to calibrate partial least squares (PLS) models are not conserved in future samples, these samples will show incorrect and biased FA predictions. This was demonstrated by using samples of one breed to calibrate and samples of the other breed to validate PLS models for individual FA. The 2 breeds had different covariance structures between individual FA and total fat content. The results showed that the validation samples yielded biased predictions. This may limit the usefulness of FT-IR–based predictions of individual FA in milk recording as indirect covariance structures in the calibration set must be valid for future samples. Otherwise, future samples will show incorrect predictions.     

傅里叶变换衰减全反射红外光谱测定米粉中硒代胱氨酸的硒含量

利用傅里叶变换衰减全反射红外光谱(ATR-FTIR)法测定米粉中硒代胱氨酸的硒含量,以期探寻一种快捷、绿色环保且准确的硒含量测定方法。采用添加法制备样本,衰减全反射中红外光谱仪采集红外光谱,TQ Analyst分析软件作偏最小二乘法建模并验证,选择1 600～650 cm~(-1)作为样品的红外光谱分析波段,用导数对原始红外光谱处理,结果表明二阶导数处理后的模型,噪音小、失真少且预测效果更好,模型的决定系数(R■)都大于0.995、因子数为5～8、校正均方差(RMSEC)为1.17～2.68,验证均方差(RMSEP)为4.053 0～6.234 6验证集预测值相对偏差在20%以内的占87.95%,含量在18μg/100 g以上更是占了96.75%,含量在50μg/100 g(不含)以上的样品93.33%偏差在10%以内,预测效果较准确。     

3种植物油傅里叶变换红外光谱信息的判别分析研究

针对花生油、大豆油和棕榈油样品,采用傅里叶变换红外光谱仪,采集红外吸收光谱,对光谱预处理后,提取红外特征信息,以1 746 cm-1和2 855 cm-1波数处吸收峰特征参数比值为X轴, 1 099 cm-1与1 119 cm-1波数处吸收峰特征参数的比值为Y轴,及3种油O-C-C对称伸缩振动各自所产生的吸收带所在波数(cm-1)值作为Z轴,绘制三维分布图,对这3种油样进行判别分析.结果显示这3种油样之间有明显区分.     

Application of Fourier transform infrared spectroscopy for the quality and safety analysis of fats and oils: A review

Abstract

Fats and oils are essential food components. Their quality and safety pose major concerns for consumers and food producers because of factors such as oxidation and rancidity, excessive levels of trans fatty acid (TFA), and widespread adulteration. Thus, a rapid and easy-to-use technique must be exploited for quality parameter evaluation and monitoring to ensure the edibility, safety, and quality of fats and oils. In the last decades, Fourier transform infrared (FTIR) spectroscopy has shown great potential in analyzing fats and oils given its speed and simplicity. FTIR-based analytical techniques for common intrinsic quality parameters, including peroxide value, free fatty acid, moisture, TFA, iodine value, as well as oxidation stability, adulteration, and classification of various fats and oils, are summarized in this review. The advantages and disadvantages of selected infrared spectral accessories and sample preparation and spectral processing methods are highlighted. The prospects and reformative aspects for future application of the FTIR technique in the field of fats and oils are also discussed. This review may serve as a basis for applying FTIR not only in future research but also in the fat and oil industries.     

傅立叶近红外光谱法测定桃果中果胶含量

目的 基于傅里叶近红外光谱(Fourier transform near infrared)检测桃果中果胶含量的研究。方法 近红外光谱采集样品利用两个品种的桃,探究光谱预处理对建模的影响,建模采用偏最小二乘法(PLS)以及主成分回归(PCR)方法,模型的评价标准采用建模相关系数(R_C)、建模均方偏差(RMSE_C)、预测相关系数(R_P)、预测均方偏差(RMSE_P)。结果 两个品种的近红外光谱图和果胶含量无明显差异(P＞0.05),采用标准正态变量变换(SNV)和多元散射校正(MSC)对原始光谱的光程进行选择,所得建模结果影响基本一致,合适光谱数据格式以及平滑处理,能提高PLS和PCR模型的预测精度和稳定性。综合得出模型最佳是利用PLS方法建模并采用MSC/SNV结合一阶导数和 Savitzky-Golay (S-G)平滑对近红外光谱图进行预处理,评价参数分别为R_C=0.7795、R_P=0.7545、RMSE_C=0.0933、RMSE_P=0.0534和R_C=0.7800、R_P=0.7530、RMSE_C=0.0932、RMSE_P=0.0534。结论 该方法为利用近红外建模快速检测桃果中果胶含量提供重要依据。     

Utilization of fourier transform infrared spectroscopy for measurement of organic phosphorus and bound calcium in cheddar cheese

The methods available for measuring organic P and bound Ca in cheese are either cumbersome or involve dilution of the cheese. Dilution of the cheese can lead to erroneous results, particularly in the case of bound Ca. Hence, the objective of this study was to evaluate the feasibility of Fourier transform infrared (FTIR) spectroscopy for direct measurement of organic P and bound Ca in Cheddar cheese. Two hundred sixteen samples of cheese were analyzed for protein-bound organic P, bound Ca using a water-extraction based method, and buffering curves. Additionally, the infrared spectra of the cheeses were collected between 4,000 and 650 cm⁻¹, at a resolution of 4 cm⁻¹, and 256 scans per sample. The spectral shifts in the infrared region from 1,050 to 900 cm⁻¹, in addition to the measured concentrations of organic P, bound Ca, and buffering peak area at pH 5.1, were used to develop calibration models using partial least squares (PLS) regression analysis. The spectral region of 956 to 946 cm⁻¹ correlated with the measured concentrations of organic P and the overall PLS model had a correlation (R²) of 0.76 between the predicted and measured concentrations. The spectral region at ∼980 cm⁻¹ was correlated with the measured concentrations of bound Ca, and the overall PLS model had a correlation (R²) of 0.70 between the predicted and measured concentrations. A similar spectral region at ∼980 cm⁻¹ was also correlated with the measured buffering peak areas and the overall PLS model had a correlation (R²) of 0.64 between the predicted and measured peak areas. A linear regression analysis between the bound Ca and buffering peak area demonstrated that bound Ca was correlated (R² = 0.73) with buffering peak area. This study demonstrates that FTIR can be used to measure organic P in cheeses. It also has the potential to be used for measuring bound Ca in undiluted cheeses, and for prediction of the buffering capacity of cheese.