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Abeverage and other areas, and mainly looks at the literature published in the last 10 years. The topics covered emphasize the methodsdesigned for on/in-line measurement of data, chemometric treatment, as well as interpretation of the experimental observations. Finally,

2.2. Fruits and vegetables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305

* Corresponding author. Tel.: +86 571 86971140; fax: +86 571 86971885.E-mail address: ybying@zju.edu.cn (Y. Ying).

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Journal of Food Engineering 87 (2008) 3033132.3. Grain and grain products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3072.4. Dairy products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3082.5. Oils . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3092.6. Fish and fish products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3092.7. Beverages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3102.8. Others . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3102.9. Constraints of NIR techniques in food analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3102.10. Conclusions and future outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 310Acknowledgements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311problems relating to the successful applications of on/in-line NIR spectroscopy in production processes have been briey outlined. 2008 Elsevier Ltd. All rights reserved.

Keywords: Near infrared spectroscopy; Foods and beverages; Quality; On/in-line; Process monitoring

Contents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3041.1. NIR spectroscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 304

2. Applications in food systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3042.1. Meat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 304Over the past 30 years, on/in-line near infrared (NIR) spectroscopy has proved to be one of the most ecient and advanced tools forcontinuous monitoring and controlling of process and product quality in food processing industry. A lot of work has been done in thisarea. This review focuses on the use of NIR spectroscopy for the on/in-line analysis of foods such as meat, fruit, grain, dairy products,0260-8

doi:10.ctbstrain foods and beverages: A review

Haibo Huang, Haiyan Yu, Huirong Xu, Yibin Ying *

College of Biosystems Engineering and Food Science, Zhejiang University, 268 Kaixuan Road, Hangzhou 310029, China

Received 17 June 2007; received in revised form 17 December 2007; accepted 22 December 2007Available online 8 January 2008Near infrared spectroscopy for on/in-line monitoring of qualityReview774/$ - see front matter 2008 Elsevier Ltd. All rights reserved.1016/j.jfoodeng.2007.12.022

transmittance through 1 cm thickness of most samples is

Meats are very susceptible to spoilage and are alsoexpensive as compared to other food types. Hence, therehas been a considerable interest in measuring their compo-sition and quality, in order to improve the eciency of unitoperations applied in meat processing (Hildrum et al.,2004). From an industrial and marketing perspective, themajor raw materials in the processing of meat are carcassesof beef and pork. NIR analysis is capable of rapid assess-ment of fat, water, protein, and other parameters simulta-neously (Clark and Short, 1994; Hildrum et al., 1994;Isaksson et al., 1995; Alomar et al., 2003; Geesink et al.,2003; Prevolnik et al., 2005; Prieto et al., 2006; Savenijeet al., 2006). However, NIR technique had not been usedfor on/in-line detections of meat until 1996.

The rst on-line application of this technique wasreported for determination of fat, moisture, and proteincontents in ground beef (Isaksson et al., 1996) on a con-veyor using a diuse NIR instrument set at the outlet ofthe meat grinder (Fig. 1), using multiple linear regression(MLR) as the calibration method. Besides being feasible

d E1. Introduction

During the last 50 years, there has been a lot of emphasison the quality and safety of the food products, of the pro-duction processes, and the relationship between the two(Burns and Ciurczak, 2001). These requirements call foron-line detection techniques which have the followingadvantages: (i) can be assembled in the production lineand take place under realistic environment, (ii) early detec-tion of possible failures, (iii) permanent monitoring of theconditions, (iv) assessment of conditions at any desiredtime (Pemen et al., 1998). These advantages enable detec-tion of quality changes of raw materials and nal productunder steady process conditions Compared to other nonde-structive techniques, NIR spectroscopy does not need anysample preparation. Hence the analysis is very simple andrapid, which is a requirement for on-line application. Fur-thermore, NIR technique allows several constituents to bemeasured simultaneously. Finally, the relatively weakabsorption due to water enables high-moisture foods tobe analyzed (Osborne, 2000). All these properties makeNIR technique widely acceptable in recent years as oneof most promising on/in line detection methods in foodand other areas.

Industries involved with foods and beverages havetraditionally used NIR measurements for quality control,blending, and process control (Workman et al., 1999).Developments in computer science and chemometrics haveprompted parallel developments in the on/in-line NIRtechniques, and have attracted considerable attention fromfood researchers. For example, this technique was appliedfor on-line detecting fat, moisture, and protein content dur-ing meat processing (Isaksson et al., 1996). With respect tograins, some researchers have installed NIRS equipment inthe harvester for continuous detection of parameters char-acterizing grain quality such as protein and moisture con-tent (Maertens et al., 2004). These on/in-line applicationshave established their control capability in food processing.

1.1. NIR spectroscopy

NIR spectroscopy is based on the absorption of electro-magnetic radiation at wavelengths in the range 7802500 nm. NIR spectra of foods comprise broad bands arisingfrom overlapping absorptions corresponding mainly toovertones and combinations of vibrational modes involv-ing CH, OH, and NH chemical bonds (Osborne,2000). This makes it very feasible for measurements to bemade in organic and biological systems. Radiation inter-acting with a sample may be absorbed, transmitted orreected. Thus, there are dierent NIR spectroscopy mea-surement modes tting dierent applications. In practice,the common modes are transmittance, interactance, trans-ectance, diuse transmittance, and diuse reectance,with the last two being most frequently used. Diuse trans-

304 H. Huang et al. / Journal of Foomittance measurements are usually carried out in theregion of the spectrum between 800 and 1100 nm wherenegligible. This situation is called diuse reectancebecause most of the incident radiation is reected. Thismeasurement is suitable for thicker samples such as fruitsand wheat power.

2. Applications in food systems

2.1. Meatweak absorptions enable useful data to be obtained usingsample thickness of 12 cm, such as with meat, cheese orwhole grain. In the wavelength range 11002500 nm, theamount of scattering makes the path length so high that

Fig. 1. Illustration of an on-line NIR instrument with the MM55 gaugemounted at the outlet of a meat grinder (Isaksson et al., 1996).

ngineering 87 (2008) 303313for on-line determination of parameters of meat quality,the amount of sample required was relatively small. Tgersen

d Eet al. (1999) extrapolated this concept to determine fat,water, and protein content in beef and pork in industrialscale batches. Although the amount of sample was muchlarger than that reported by Isaksson et al. (1996), the pre-diction errors were similar. Thereafter, with the sameequipment by reectance spectroscopy, Tgersen et al.(2003) determined fat, water, and protein content in semi-frozen raw meat, which is largely used in the manufactur-ing industry, due to mismatches between supply anddemand of raw meat in the market.

A NIR reectance instrument with a diode array detec-tor was applied for in-line monitoring of the proximal com-position of ground beef on a conveyer belt. (Hildrum et al.,2004). This technique makes it possible to perform mea-surements over large meat surface areas on each batchunder industrial conditions. Sixty batches of coarselyground beef were processed under industry conditionsand monitored continuously. After removing signals origi-nating from the belt itself, the remaining data were used toform partial least squares (PLS) models for each compo-nent in beef at two dierent sizes. The results showed thatthe predictions were generally better when using smallergrinding size. In addition, a forward variable selectionmethod based on jack-kning was used, and obtainedsimilar results. A NIR transmission system for on-line mea-surement of fat in unhomogenized meat has been reportedby Schwarze (1997). The system was used for the continu-ous analysis of meat products with varying compositionand particle size during mixing process. Samples were auto-matically extracted, analyzed and fed back into mixer. Theresults showed NIR spectroscopy to be suitable for on-linemonitoring fat contents during meat processing.

Modern blending operations use two blending steps toachieve a target fat content, whereas only one step wouldbe required if the fat content of the unblended heteroge-neous stream of ground beef were determined on-line. Withthe purpose of eliminating blending steps and improvingthe quality of blended beef, Anderson and Walker(2003a) applied the Perten DA700 NIR/VIS analysis sys-tem for measuring fat content in ground beef in the movingstream of meat formed by a grinder equipped with a cus-tom forming head. The measurements on 27 kg blocks ofbeef achieved high accuracy with SEP (Standard Error ofPrediction) of 1.001.68% for the calibration set and2.152.28% for the validation set. However, it did not showhow or whether these measurements could eectively elim-inate a blending step. Thus, thereafter, a simulation wasused by Anderson and Walker (2003b) to demonstratethe ecacy of on-line visible/near infrared spectroscopicmeasurements of fat content in the streams of ground beefto achieve a target fat content in a nal blender. Threestyles of control limits (constant limit, tapered limit, andfunnel limit) were tested for the model, which was cali-brated by more than 10,000 spectra from 31 blocks of fro-zen beef. The control limits for these styles achieved the

H. Huang et al. / Journal of Footarget fat content with a tolerance of 0.75% fat, 99.7% ofthe time.On-line analysis of fatty acids (C14:0, C16:0, C16:1,C17:0, C17:1, C18:0, C18:1, C18:2, C18:3, and

Ppolyun-

saturated,Pmonounsaturated and

Psaturated) in the

intramuscular and subcutaneous fat in Iberian pork loinhas been successfully achieved using NIR spectrometerequipped with a remote reectance ber-optic probe (Gon-zalez-Martn et al., 2003, 2005). In addition to predictingchemical parameters, visible and NIR spectroscopy wasalso used for on-line analysis of tenderness of longissimussteaks during commercial beef carcass grading (Shackel-ford et al., 2004).

Interference by signals caused by uneven surface ofmoving meat poses a serious problem during on-line mon-itoring of ground beef composition on a conveyor beltusing a NIR reectance sensor. Thus, no good calibrationmodels were obtained with the original raw spectra. Wes-tad et al. (2004) developed a soft independent modelingof class analogies (SIMCA) classication method for yield-ing pure meat spectra, which was shown to be useful.

NIR spectroscopy could also used to determine sodiumchloride (NaCl) in cured meat. As early as 1984, Begleyet al. (1984) applied the NIR technique to measure theamount of NaCl in canned cured hams. A high correlationbetween salt content determined by chemical analysis andby NIR spectra at 1806 nm was obtained. Finally, theyconcluded that the ability of NIR to measure salt contentwas due to a shift in the water spectrum caused by salt-induced changes in the amount of hydrogen bonding.

2.2. Fruits and vegetables

Fruit and vegetables are a unique class of food items in asense that their size, colour, shape, and chemical composi-tion vary, even when harvested at the same place and sametime. Hence, sorting them on the basis of their quality isvery important. Use of conventional analytical techniquesis very time-consuming and labour intensive. NIR spectros-copy is an attractive non-destructive technology well-suitedto the measurement of moisture in fruit and vegetables(Kays, 1999). Determination of quality parameters undero-line conditions using NIR instrumentation has beenreported previously (Slaughter et al., 1996; Shen et al.,1998; Hart et al., 1998; Lu, 2001; Terasaki et al., 2001;Liu and Ying, 2004; Walsh et al., 2004; Gomez et al.,2006), providing an impetus for the development of on-linemonitoring and grading techniques.

Kawano et al. (1992, 1993) used NIR spectroscopy fordetermination of sugar content in intact peaches and man-darins, and reported an automated fruit sorting machinebased on this principle. Since then, on-line NIR has beenwidely applied in fruit and vegetable processing. Choi(1998) developed an on-line machine based on NIR reec-tance spectroscopy for real time determination of sugarcontent at a sorting speed of two fruits per second. A goodresult with a low SEP of 0.78 Brix was obtained in Fuji

ngineering 87 (2008) 303313 305apples; such errors were acceptable for rapid on-line detec-tion. High-resolution laboratory-based spectrometers are

commercially available, but are generally expensive and notfeasible for on-line integration in an industrial process.Greensill and Newman (2001) reported the performanceof three simple wavelength dispersion elements (single equi-lateral prism, two equilateral prisms in series, and ruled dif-fraction grating) for the design of a simple, low-cost, androbust NIR spectrometer for application in automatedfruit grading systems. They found all the designs to per-form well; the dual-prism instrument demonstrated thehighest potential for reliable, rapid sorting of the fruit thanthe other two types.

He et al. (2001) compared three NIR measuring meth-ods: the on-line reex, the partially shaded light transmis-sion and the fully shaded light transmission. Theydetected sugar content, acidity, and internal browning inoranges and apples by a fully shaded light transmissiondetecting device. Satisfactory results were obtained withR2 of 0.95 for Brix, and 0.85 for acidity. Both the on-linecommercial NIR equipment and hand-held NIR units wereused to measure Brix level of Florida citrus by Miller andZude (2002). For the determination, spectral data wereobtained using two light sources mounted on a specialcup, placed against the fruits surface. The on-line testswere conducted at a rate of 5.5 fruits per second. For cal-ibration, linear regression relationships were developedbetween the non-destructive NIR techniques and the labo-

ratory Brix measurements. However, all R2 values werelower than 0.7. Then, they evaluated a neural networkmodel with combined inputs of physical and colour attri-butes and predicted Brix using NIR. Correct classicationaccuracy was 90% for 10Brix, and 80% for 11Brix setpoint.

Internal browning is a disorder that aects many varie-ties of commercial apple cultivars including Braeburn,Sunrise, Fuji, Red Delicious and Golden Delicious(Elgar et al., 1999; Lau and Lane, 1998; Volz et al., 1998;Keener et al., 1999). Hence, development of accuratenon-destructive test methods for on-line inspecting of fruitsfor internal browning and removing them from consign-ments is a long-felt necessity. Clark et al. (2003) appraisedthe use of NIR transmittance to segregate Braeburnapples aected by a full range of browning, by applying dif-ferent analytical techniques to fruit in dierent orienta-tions, and concluded it to be suitable for sorting fruits,and thereby reduce the incidence of Brownheart in com-mercial consignments. Thereafter, practical prototype sys-tems were constructed and tested (McGlone et al., 2005).These systems demonstrated an accurate measurement ofBrownheart in fruits moving at realistic grading speeds.Two specic on-line transmission systems (a time-delayedintegrating spectrometer (TDIS), and a large aperture spec-trometer (LAS) (Fig. 2) were constructed and compared.

s th

306 H. Huang et al. / Journal of Food Engineering 87 (2008) 303313Fig. 2. A conceptual view of NIR transmission system. As the fruit passe

simultaneously accumulates many sequential points over three apples. In contrashorter time for a small portion of one fruit (McGlone and Martinsen, 2004).rough a relatively large eld-of-view in the TDIS system (A), a detector

st, the LAS system (B) takes a simple snapshot, like a camera, over a much

These two systems were each optimally congured to oper-ate at typical grader speeds (500 mm s1 or approximatelyve fruit per second), and detect the low levels of light dif-fusely transmitted through apples in the wavelength range650950 nm. Finally, they concluded LAS system to givebetter results. In addition, these two systems were previ-ously tested by McGlone and Martinsen (2004) for theirability to measure dry matter (DM) in apples. Both thesegave excellent predictions with standard errors of less than0.5% at the speed of 500 mm s1. NIRS technique was usedby Golic and Walsh (2006) to sort stone fruits (peaches,nectarines, and plums) on the basis of total soluble solidsin an in-line setting. Mixed nectarinepeach calibrationand plum models performed well in predicting of total sol-uble solids (TSS) in nectarines and peaches, and plums,

H. Huang et al. / Journal of Food Erespectively. The calibration set samples were scanned atdierent temperatures (5 and 20 C) over several seasonsto conrm the robustness of these mixed models.

Hahn (2004) explored NIR spectral bandwidth eect onRhizopus stolonifer spores detector and its on-line behaviorduring classication of red tomatoes. The NIR spectrawere acquired before and after inoculating tomatoes inthe laboratory. Discriminant analysis carried out at 5, 2,and 1 nm wide spectral bandwidths showed 1 nm band-width to possess the highest accuracy (88.92%). When thesame was used for on-line classication on an automaticconveyor, a 92% detection accuracy was encountered fora spore count of 6.5 104 sporangiospores ml1.

Xie et al. (2007b) used Vis/NIR diuse reectance spec-troscopy combined with multivariate analysis to dierenti-ate 70 transgenic tomatoes and 94 of their parents. PCA,discriminant analysis (DA), and PLSDA were applied toclassify these tomatoes with dierent genes into twogroups. After comparison, PLSDA model with the leave-one-out cross-validation technique after second derivativepre-treatment gave the most satisfactory calibrationand prediction ability. Thereafter, Vis/NIR diuse trans-mittance spectroscopy, in combination with dierentchemometrics was used to distinguish transgenic andnon-transgenic tomatoes (Xie et al., 2007a). PCA, SIMCA,Fig. 3. Measurement conguration on the bypass of the elevator(Maertens et al., 2004).discriminant partial least squares (DPLS) regression basedon PCA scores were applied to classify these transgenic andnon-transgenic tomatoes. When using DPLS after pretreat-ment of second derivative method, the accuracy couldreach 100%, showing Vis/NIR technique to be an eectivemethod to dierentiate objects with similar properties.

2.3. Grain and grain products

Grains including wheat, rice, and corn are main agricul-tural products in most countries. Grain quality is animportant parameter not only for harvesting, but also forshipping (Burns and Ciurczak, 2001). In many countries,the price of grain is determined by its protein content,starch content, and/or hardness, often with substantialprice increments between grades. Several studies showgrain quality parameters to be signicantly variable, evenwhen harvested in the same eld and at the same time(Reyns et al., 2000; Bramble et al., 2002). NIRS technologyhas made it possible to directly measure dierent constitu-entsin the grain products (Wehling et al., 1996; Delwiche,1998; Campbell et al., 1999; Kawamura et al., 1999; Ber-ardo et al., 2005; Ozdemir, 2006). Furthermore, its abilityto be installed on the harvesting machine itself is advanta-geous for on-line determination and grading.

Engel et al. (1997) described an approach for inspectinggrain protein on-line by the use of NIR analysis. On-linemeasurement of grain quality with respect to moistureand protein content by a NIR measurement device (Maer-tens et al., 2004) that was installed in a bypass unit of theclean grain elevator in a conventional combine harvesterhas been possible (Fig. 3). The calibration models betweenNIR spectra and quality parameters were developed byPLS algorithm and validated through cross-validation,with standard error of cross-validation (SECV) of 0.57%and 0.31% for protein and moisture content, respectively.Thereafter, with similar equipment, Montes et al. (2006)examined the potential of NIR on combine harvest fordetermination of dry matter, crude protein, and starchcontent in maize grain. NIR spectra were collected over arange of 9601690 nm with the interval of 6 nm, Theinstrumentation was calibrated by using modied partialleast squares (MPLS). In addition, calibration models fordetermination of dry mater, starch content, in vitro digest-ibility by cellulase, and soluble sugars in maize foragebased on NIR measurements taken directly on the chopperduring harvest were developed (Welle et al., 2003). Using anetwork of six diode arrays, NIR spectrometers wereimplemented successfully for on-line analysis of dry mattercorn grain (Welle et al., 2005). With this method, calibra-tion models were derived from the database of spectra fromall six instruments; this eliminated the need to apply spe-cic standardization algorithms when using dierent NIRinstruments.

In Japan, Kawamura et al. (2003a) developed another

ngineering 87 (2008) 303313 307type of automatic rice-quality inspection system using aNIR instrument and a visible light segregator. In the

not suitable for milk powder and other dairy products(Rodriguez-Otero et al., 1997). With developments in com-puter programming and chemometrics, NIR analysis hasbeen widely used in this area. Furthermore, when coupledwith ber-optics, it can be successfully used for on-line con-trol in the production line.

Kawamura et al. (2003b) constructed a NIR spectro-scopic sensing system for on-line predicting of three majormilk constituents (fat, protein, and lactose), somatic cellcount, and milk urea nitrogen in uid milk. This systemconsisted of an NIR instrument, a milk ow meter and amilk sampler (Fig. 4), taking the diusion transmittancespectra in the range of 6001050 nm at 1 nm intervals every10 s during milking. This system can be used for real-timemonitoring of quality parameters during milking with suf-cient precision and accuracy. With the same equipments,a further study (Kawasaki et al., 2005) to improve the

d Engineering 87 (2008) 303313system, the NIR transmission was used to determine mois-ture and protein content of the samples while the Vissegregator was used to determine sound whole kernel ofbrown rice. This system enabled rough rice transportedto a rice-drying facility to be classied into six qualitativegrades.

De Temmerman et al. (2007) applied NIR spectroscopyfor in-line determination of moisture concentrations insemolina pasta immediately after the extrusion process.Reectance spectra between 308 and 1704 nm wereacquired at the extruded die. PLS regression method wasused to develop an adequate prediction model for the in-line moisture content. The best cross-validation resultswere obtained for non-transformed data. The resultsindicated that NIR spectroscopy could be used for processcontrol in the pasta industry.

Besides qualitative and quantitative chemical analysis,NIR spectroscopy technique could also be used forfood structure determination. Bruun et al. (2007) appliedNIR spectroscopy for monitoring changes in gluten pro-tein structures and interactions when the gluten power ismodied by increasing water content and heat treatment.Second-derivative transformation and extended multiplica-tive spectral signal correction were used as pretreatments ofspectra, in order to improving the band resolution andremoving physical and quantitative spectral variations.Then PCA and PLS regression method were appliedfor making classication and calibration models. Theresults showed NIR spectrum to be able to give importantinformation on structure changes in gluten proteins,including secondary changes. Thereafter, with the similarmethod mentioned above, the same authors applied NIRspectroscopy for analysis of protein structures and interac-tions in hydrated gluten, and obtained satisfactory results.

Kays et al. (1996) used NIR spectroscopy for the predic-tion of total dietary ber in food. Cereal and grain prod-ucts, including breakfast cereals, ours, bran, crackers,and samples containing commercial oat and wheat bers,were selected for analysis. These samples were dry milled,and scanned with a NIR spectrometer in the bandwidthrange 11002800 nm. PLS regression method was appliedto develop the models. The results showed that NIR satis-factorily predicted the total dietary ber content in a widerange of cereal products.

2.4. Dairy products

Traditional reference analytical procedures for mois-ture, fat, protein, and lactose in dairy products are timeconsuming, expensive, need trained manpower, and failto comply with requests in modern industry. In order tosolve this problem, a lot of instrumental analytical tech-niques have been developed. From 1980s, middle-infraredspectroscopy technique permitting detection without anyprevious treatments, and in the absence of chemical regents

308 H. Huang et al. / Journal of Foohas indeed revolutionized the dairy laboratories (Kennedyet al., 1985; Luinge et al., 1993). However, this technique isrobustness of calibration models for on-line measurementof milk quality items using NIR spectrum data were con-ducted from two dairy herds. It was found that when cali-bration models developed from data acquired from oneherd were used for validation of data from the same herd,the milk quality items could be measured with high levelsof accuracy. However, when the calibration models wereused for validation of data obtained from the other herd,the accuracy in measurements of all milk analytes exceptfat was low. Thus, it is very important to use a combinedvariable sample to develop a robust calibration model. ANIR microspectrometer system for on-line monitoring offat during milk processing has also been developed (Bren-nan et al., 2003). This system used Microsystems opticalcomponents fabricated using the LIGA technique. Thesespectrometers have been widely used for color and qualityanalysis in diamonds, but seldom used for on-line process-ing detection. NIR spectra were obtained in the range of8001100 nm. Evaluation of a number of regression modelsshowed Ridge regression techniques to give best results.Specially designed ber-optics for on-line NIR measure-Fig. 4. An on-line NIR spectroscopic sensing system (Kawamura et al.,2003).

d Ement of 222 rumen uid in milking cows (Turza et al.,2002) has been developed. This system could improve thequality of milk, since components in cows rumen can beconsidered as precursors for milk production in milkingcows.

NIR analysis has also been used in cheese making.Adamopoulos et al. (2001) applied NIR spectroscopic tech-nique for process control of traditional feta cheese duringproduction. NIR spectra were obtained at wavelengths of1940, 2180, and 2310 nm relating to moisture, protein,and fat, respectively. The calibration models were devel-oped by a suitable computer program and validated usingan independent set of analyzed samples. Thereafter, Mer-tens et al. (2002) proposed a statistical model based onNIR for real-time prediction of cutting time in cheese man-ufacture, since the cutting point is very important for theprocess of curd forms (Laporte et al., 1998).

In addition, NIR spectrometry combined with electronicnose (EN) data have been used for on-line monitoring ofyogurt and lmjolk (a Swedish yogurt-like sour milk)fermentations under industrial conditions (Navratil et al.,2004).

2.5. Oils

Oils are very important food groups. Conventional ana-lytical methods for measuring the oxidation and adultera-tion of oil are time consuming, destructive, expensive,require chemical reagents, and are laborious. NIR spec-troscopy technique has many applications in this area.

Yildiz et al. (2001) applied NIR spectroscopy for moni-toring oxidation levels in soybean oils. Peroxide value(PV), conjugated diene value (CD), and anisidine value(AV) in soybean oils were quantitatively determined. Forthe determination, PLS regression and forward stepwisemultiple linear regression (FSMLR) combined with rstderivative and second derivative methods were used todevelop models. They concluded that wavelengths in the11002200 nm regions were most useful for prediction,and PLS regression using rst derivative spectra gave thebest results for PV. However, as opposed to PV and CD,measurement of AV by NIR was not as well as expected.Thereafter, they (Yildiz et al., 2002) determined PV in cornand soybean oils by NIR spectroscopy technique. Whenthe calibration models developed by PLS regression of rstderivative spectra were used to predict validation sets con-taining equal numbers of corn and soybean oil samples,good results were obtained. Later, NIR spectroscopy wasused for measuring degradation products in frying oils,including total polar materials (TPMs), and free fatty acid(FFAs), which have a negative eect on the avor andnutritional value of fried products (Ng et al., 2007). PLSand FSMLR were used to develop models. The authorsfound that when using a wavelength at 7001100 nm,PLS models gave better results than FSMLR models.

H. Huang et al. / Journal of FooMoreover, the derivative treatments had limited utility,especially in the longer wavelength regions (11002500 nm). This method could be adapted to an automated,continuous-ow sampling system.

Visible and NIR spectroscopy was used for detectingand quantifying sunower oil adulteration in extra virginoils (Downey et al., 2002). One-hundred and thirty-eightoil samples were analyzed by Vis/NIR transectancespectroscopy. A number of mathematical methods wereinvestigated to detect and qualify the sunower oil adulte-ration, including hierarchical cluster analysis, SIMCA, andPLS. The accuracy of these mathematical models was com-pared. SIMCA could successfully discriminate betweenauthentic extra virgin olive and the same oils adulteratedwith sunower oil at levels as low as 1% (w/w). Once adul-teration was detected, PLS was used to quantitativelyanalyze the sunower contents. The results showed thatthis level of accuracy was acceptable for industrial use.Vis/NIR transmittance spectroscopy was used by Marquez(2003) to determine the total levels of chlorophyll andcarotenoid in virgin oil. An initial smoothing techniquecombined with rst derivative treatment was used to cor-rect the signal. PLS regression was used to develop calibra-tion models, which were used to monitor on-line levels ofthese compounds during virgin olive oil processing in oliveoil mills. Satisfactory results were obtained. Thereafter,Marquez et al. (2005) applied NIR transmittance spectros-copy for on-line detection of acid value, bitter taste (k225),and fatty acid compositions in virgin olive oils. NIR spec-tra were obtained in the wavelength range of 7502500 nm.A 1 mm optical path length ow cell with a sample volumeof 120 ll was used. PLS regression was used to developmodels for on-line prediction of all these characteristics.

2.6. Fish and sh products

Uddin et al. (2002) applied NIR spectroscopy to assessthe end-point temperature (EPT) of heated sh and shell-sh meats. In this research, blue marlin, skipjack, red seabream, kuruma prawn and scallop meats were heat-treatedat dierent temperatures. NIR spectra were measured atthe wavelength range of 11002500 nm at 2 nm intervals.For calibration, stepwise multiple linear regression wasused to develop models. The inference of the water contentin the sh meats on the performance was eliminated byselecting appropriate wavelength. A promising linear rela-tionship between the EPT and NIR-predicted temperatureswas obtained, revealing the ability of NIR spectroscopy tomonitor EPT during the sh and shellsh heating. Thereaf-ter, NIR spectroscopy was used to verify EPT of kam-aboko gel (Uddin et al., 2005). PLS and MLR were usedto develop model which was tested with validation set.The result showed that NIR-predicted EPT and actualheating temperatures revealed a linear relationship. Themodels developed by PLS and MLR had similar perfor-mance for predicting EPT of kamaboko gel when usingappropriate wavelength range.

ngineering 87 (2008) 303313 309A muli-spectral imaging NIR transectance system wasdeveloped for on-line determination of moisture content in

d Edried salted coash (bacalao) (Wold et al., 2006). The com-bination of NIR transectance measurement with spectralimaging allows rather deep penetrating optical samplingand large exibility in spatial sampling patterns and calibra-tion approaches. In addition, the technique of reectance,contact transectance and non-contact trandectance werecompared with a small set of dried salted cod samples.The result showed the last two were superior to reectancemeasurements.

2.7. Beverages

NIR technique has been used for on-line determinationof constituents in alcoholic beverages such as beer, wine,and distilled spirits; nonalcoholic beverages such as fruitjuices, teas, and soft drinks; and other products such asinfant and adult nutritional formulas. Some of the applica-tions are described below.

Recently, Zeaiter et al. (2006) applied Vis/NIR spectros-copy to the study of on-line monitoring the alcohol contentduring alcoholic wine fermentation. For the determination,samples were scanned in transmission mode over the rangeof 2002500 nm at 2 nm intervals using aNIR spectrometer.For calibration, PLS regression method was used to developthe models. In order to correct prediction model used inspectroscopy-based process monitoring, a new methodcalled dynamic orthogonal projection (DOP) was applied.The results showed this method to improve the robustnessof the calibration model. NIR spectroscopy combined withmultivariate analysis (PCA, DPLS, and linear discriminantanalysis (LDA)) has been used for in-line monitoring theprogress of red wine fermentation in a pilot scale (Cozzolinoet al., 2006). Samples (n = 652) were collected at dierenttimes from several pilot scale fermentations, and scannedin transmission mode with the spectra range between 400and 2500 nm. They used PCA to demonstrate consistentprogressive spectral changes that occur through the timecourse of the fermentation. Linear LDA showed that regard-less of variety or vintage, samples belonging to a particulartime point in fermentation could be correctly classied.

In addition, continuous processing of apple, grape, pear,applecherry and applebanana juices for soluble solidsand total solids/total moisture can also be assessed (Singhet al., 1996). Three in-line sensors: NIR, guided microwaveand Maselli refractometer were compared for their in-lineperformance of testing. The result showed NIR and guidedmicrowave to be good for assessing the soluble and totalsolids, and Maselli refractometer to be excellent for pre-dicting soluble solids under dierent operating conditions.Leon et al. (2005) applied NIR transectance spectroscopyfor detection of adulteration of apple juice samples. Twotypes of adulterants were assessed: a high fructose cornsyrup (HFCS) and a sugar solution. DPLS regressionmethod was used. The results showed that the accuracyof detection of authentic apple juice and adulterated apple

310 H. Huang et al. / Journal of Foojuice were 86100% and 91100%, respectively, dependingon the adulterant type and level of adulteration.2.8. Others

On-line application using NIR methods on other kindsof food are also known. Although these foods are notamong the ve categories mentioned above, the methodol-ogy and interpretation there from are very important andshould not be ignored. NIR methods have been used foron-line viscosity and conductivity measurements in frozenmodel sorbet in a continuous freezer/extrusion process(Bolliger et al., 1998). Monitoring of colour and composi-tion in an extruder during the extrusion of yellow cornour (Apruzzese et al., 2000), and on-line classication ofpoultry carcass quality (Chen et al., 2003) by Vis/NIRspectrophotometer system has been known. In baking area,Sinelli et al. (2004) used FT-NIR spectrometer with anoptic probe for monitoring the kinetics of dough proongand bread staling.

2.9. Constraints of NIR techniques in food analysis

Although the operating cost of NIRS is low, the instru-ment itself is highly priced; this limits its practical applica-tion. Eorts by researchers and industrial organizations todevelop simple and low-cost instruments could revolution-ize the use of NIR techniques for on/in-line quality moni-toring of foods.

Some calibration models based on NIR spectroscopy,especially for on-line application, are not reliable and sta-ble enough when used practically. Hence, it is imperativefor researchers to choose proper chemometrics to buildrobust models. In some cases, conventional methods maynot oer a satisfactory solution to a given problem dueto complexity of the data. This also necessitates the devel-opment of new chemometric methods so as to furtherimprove the reliability and accuracy of the calibrationmodels.

In addition, there are other limitations of NIR spectros-copy technique. The technique is not sensitive to themineral content, since there is no absorbtion of mineralsin the NIR spectrum region. An alternative way to solvethis problem eciently is to combine dierent detectiontechniques with NIR spectroscopy, such as X-ray uores-cence spectroscopy, UV light, and electronic nose tech-nique. Some papers describing the use of a combinationof techniques using dierent detection methods have beenpublished in recent years (Cimander et al., 2002; Navratilet al., 2004), although more eorts should be made to solvethis issue.

2.10. Conclusions and future outlook

On/in line applications of NIR spectroscopy in foodscience are reviewed in meats, fruit and vegetables, grain,and grain products, milk and dairy products, and bever-ages and other areas. At present, NIR technique is widely

ngineering 87 (2008) 303313accepted as one of the most promising on/in-line processcontrol techniques NIR is obviously a nondestructive,

d Ereliable and accurate technique for monitoring chemicaland physical parameters during food processing. Further-more, it is worth mentioning that the appearance of ber-optic probes signicantly improves the ability of NIRtechniques to monitor and control processes especiallyusing remote on/in line detection.

Acknowledgements

The authors gratefully acknowledge the nancial sup-port provided by National Natural Science Foundationof China (No. 30671197) and National Key TechnologyR&D Program (No. 2006BAD11A12).

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