Introduction

Polyphenol compounds and vitamins in food are of great interest and have gained more attention in recent years due to their health properties. Several epidemiological studies proved that these compounds have the capability to improve health by reducing risk or the symptom of chronic diseases (Shahidi and Zhong 2015). Recently, the role of flavor compounds in human health due to their antioxidant, anti-inflammatory, anti-cancer, and anti-obesity activities was reviewed by Ayseli and İpek Ayseli (2016). Minor constituents of food, especially phenolic and volatile compounds, are inherently associated with the global quality and positive sensory attributes of various foods (Inarejos-García et al. 2013). Typically, the price and ranking (i.e., high, medium, or low commercial value) of many foods depend on the organoleptic properties (Bianchi et al. 2005; D’Amico et al. 2016). Several attempts have been made to establish highly sensitive and selective approaches for measurement of bioactive and aroma components where comprehensive researches have been performed on the various extraction and separation techniques (e.g., SPE, SPME) as well as enhancing the chromatographic used (e.g., HPLC, GC-MS) (Tahir et al. 2016c; Tahir et al. 2017). Although these techniques can deliver accurate results, these methods are time-consuming, laborious, expensive, and complex (Cigic and Prosen 2009). Furthermore, the extraction process used may affect the chemical composition of phenolic compounds and/or aroma compounds (Boido et al. 2013; Bureau et al. 2019; Tahir et al. 2016a, b, c). Therefore, scientists continue to make significant attempts to find alternative rapid, ease-of-use, no or minimal sample preparation, and cost-effective techniques that might complement or replace conventional analytical tools in terms of measurement of aroma and bioactive compounds (Tahir et al. 2016a, c, 2017). Vibrational spectroscopies including infrared (e.g., FT-IR, NIR, MIR), Raman, and hyperspectral imaging (HSI) coupled with chemometric methods could be used as alternatives for quantitative analysis of various biologically active materials and aroma compounds (Fig. 1) in foods due to many advantages (Table 1). Some of these advantages include the hastening of the analysis and reducing the cost.

Fig. 1.
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The flowchart of assessing bioactive components volatile compound and antioxidant activity by vibrational spectroscopy

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Table 1 The main features of some vibrational spectroscopy techniques for measurement of bioactive compounds and volatile in foods
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Nowadays, vibrational spectroscopies have been applied as powerful tools to study molecules, aromatic, and biological compounds in food (de Toledo et al. 2012; Li et al. 2019; Sekine et al. 2011). The contribution of these compounds (e.g., chlorogenic acid, flavonoid 3-methoxyquercetin, luteolin, phenol, pigments, tannin) to FT-IR, NIR, and Raman spectroscopy data has been previously reported (Coates 2000; Eravuchira et al. 2012; Marrassini et al. 2015; Sekine et al. 2011; Tahir et al. 2017; Tondi and Petutschnigg 2015). The infrared (IR) region includes that part of the electromagnetic spectrum in the wavelength range between 780 and 100,000 nm and is divided into near-IR, mid-IR, and far-IR subregions (Baeten and Dardenne 2002; Osborne 2006); the NIR region covers the wavelength range from 780 to 2500 nm (Osborne 2006). When compared with spectra collected in the mid-infrared (MIR) region, a NIR spectrum normally exhibits few well-defined, sharp peaks (Williams and Stevensen 1990). NIR technique operates with a light source from which the sample absorbs specific frequencies corresponding to overtones and combination bands of vibrational transitions of the molecule primarily of OH, CH, NH, and CO groups (Karoui 2018). MIR spectroscopy is typically used to determine the molecular composition of a sample. Raman technique is fundamentally similar to infrared since it gives information about the chemical bonds within the organic matrix. Raman technique is based on a special phenomenon termed Raman scattering. When the incident light is directed at a sample, a small fraction of the light is scattered by the sample. The scattered light conveys information on the vibrational band energies of molecules. A plot of the intensities of the scattered portion of incident light against the shifts in the frequency between the incident and the scattered light is known as Raman spectra (Kizil and Irudayaraj 2018). After vibrational spectroscopy data obtained, chemometrics techniques are employed to extract relevant information and remove the irrelevant information in order to determine volatile compounds and other bioactive compounds accurately. Generally, chemometrics comprises two parts, spectral preprocesses and regression methods. In the current article, a comprehensive overview of applications of four technologies for evaluating bioactive and volatile compounds will be addressed and some technical challenges and future research trends will be discussed as well.

Vibrational Spectroscopy

Recently, vibrational spectroscopy–based approaches have been extensively applied for the determination of various biologically active agents and volatile compounds in foods. These techniques, representing good alternatives to conventional methods, involve the application of toxic chemical and/or complicated sample preparations. Furthermore, vibrational spectroscopy methods enable the measurement of several characteristics from a single analysis in a very short time. Moreover, modern advancement in instrumentation and computational fields, as well as development in chemometric techniques, makes vibrational spectroscopy methods available for in situ and online investigation of different kinds of samples. Three key techniques are implemented to measure molecular vibrational motions: infrared spectroscopy (near and mid-infrared), Raman spectroscopy, and hyperspectral imaging system (HSI) technique.

The intense and specific bands indicated in the Raman, MIR, and FT-IR spectra data make these technologies very interesting, enabling the measurement of quality parameters in foods. MIR and NIR spectroscopies have a good signal intensity compared with Raman technique; however, MIR has the advantage over NIR; thus, trace components can be identified. The typical detection results of optical techniques are presented in Fig. 2 by taking honey and cocoa bean as an example (Caporaso et al. 2018; Tahir et al. 2017).

Fig. 2
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(a) representative FT-IR, Raman, and NIR spectra of honey. (b) HSI spectra of cocoa Q4 beans

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Infrared Spectroscopy

Infrared (IR) is a region that presents the molecular bond vibrations, in the spectral zones between 14,000 and 4000 cm−1 for NIR and 4000–400 cm−1 for MIR, offering valuable information regarding physical and chemical characteristics of samples. NIR spectrum results from complex overtones and combinations of tones whereas the MIR spectrum represents the fundamental vibrations of molecules (Burns and Ciurczak 2001; Ozaki, McClure et al. 2006). The suitable mode of infrared spectroscopy analysis mode should be determined based on the optical characteristics of the food samples (Fig. 3).

Fig. 3
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Infrared spectroscopy assessment modes—a transmittance, b diffuse transmittance, c diffuse reflectance, and d transflectance

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Reflectance mode determination is the easiest to acquire because it can be performing without contact with the food besides light intensities are fairly high. However, it is subjected to variations in surface characteristics. The advantage of the transmission cell is that it offers very accurate and reproducible spectroscopic determinations; however, the main drawbacks often require a destructive preparation or semi-preparation of the sample. Generally, transmission mode determinations are better than reflectance mode for measurements of internal disorders of foods. However, the intensity of light penetrating the food is often very low, making it a challenge to acquire accurate transmission determination, mainly in environments of high ambient light levels.

Transparent foods are usually analyzed in transmittance (Fig. 3a). Solid and semi-solid or turbid foods could be analyzed in diffuse transmittance (Fig. 3b), diffuse reflectance (Fig. 3c), or transflectance (D) based on their absorption and scattering properties. Also, pseudo absorbance (A) relative to standard reference material is determined (A = log(1/T) for transmittance and log (1/R) for reflectance spectra) (Reich 2005).

Raman Spectroscopy

In Raman spectroscopy, the sample is illuminated with a monochromatic laser beam which interacts with the molecules of the sample and creates a scattered light. The scattered light having a frequency different from that of incident light (inelastic scattering) is used to produce a Raman spectrum (Bumbrah and Sharma 2016; Skoog et al. 2017; Smith and Dent 2019). Raman spectroscopy also provides information regarding the chemical bonds within a compound similar to those obtained from infrared spectroscopy. However, the laser sources used in Raman technique might cause fluorescence of some organic molecule compounds, affecting the signal-to-noise ratio and decreasing the sensitivity of this technique. Hence, in many studies, it was used as a complement to IR data (Gao et al. 2018).

HSI Technique

Hyperspectral imaging (HSI) is a spectral imaging acquisition, which combines the main characteristics of spectroscopic and computer vision technique. It is considered a very efficient analytical tool to meet the growing demand of both spatial and spectral information, and therefore it has been extensively used for food quality and safety inspection (Su and Sun 2018a, b). HSI technique recorded spectral information in different spectral regions and can be combined with various spectroscopies such as VIS/NIR, MIR, and Raman (Pallone et al. 2018; Su and Sun 2018a, b). VIS/NIR HSI systems are the most common for food quality and safety measurements (Cheng et al. 2017). Over the past 6 years, vibrational spectroscopic technologies, particularly NIR, FT-IR, Raman, and NIR-HSI techniques, have demonstrated their potential in the measurement of food quality parameters including biologically active materials and volatile profiles.

Some Advantages and Drawbacks of Raman and Infrared Spectroscopies

Raman and IR spectroscopies showed many advantages that make these technologies attractive as substitutions to conventional, boring, and more time-consuming analytical methodologies. They are fast, do not require chemical reagents, and are non-destructive. Both Raman and IR spectroscopic procedures are integral because of their diverse determination rules. Since infrared motion needs a change in the dipole moment of a molecule, those bonds which are highly polar will absorb the most strong, for instance, C=O and O–H. Conversely, diatomic atoms, for example, H2, will not be infrared active (Pavia and Lampman 1996). In Raman spectroscopy, it is the change in polarizability, which is critical, and thus, bonds such as C=C are the most intense. Since all of the frequencies are determined simultaneously, most measurements by IR are made in a matter of seconds rather than several minutes. For instance, the detectors utilized in FT-IR are very sensitive and the optical throughput is greatly higher, which results in very lower noise levels; and the rapid scans enable the addition of several scans (i.e., signal averaging) to decrease the random measurement noise to any required level. In FT-IR spectroscopy, the moving mirror in the interferometer is the only continuously moving part. Therefore, there is very little risk of a mechanical breakdown. FT-IR system uses a HeNe laser, as an internal wavelength calibration standard, thus, does not require to be calibrated by the user. These advantages together with others make assessments conducted by FT-IR very accurate and reproducible. The main disadvantage of FT-IR is a single beam whereas dispersive techniques commonly have a double beam (Dutta 2017).

The main advantages of NIR spectroscopy over other spectroscopies are that they can provide spectra with high intensity, high resolution, precise spectral frequency measurement, being fluorescence-free, and ease of sample presentation. Furthermore, this technique is appropriate for online and in-line process monitoring and quality control of food products. Conversely, this system is characterized by poorly resolved spectra, the lack of information from nonpolar groups, an absence of structural selectivity and sensitivity, and spectra affected by temperature changes (Fernández Pierna et al. 2018). NIR-HSI is widely used for measurements of bioactive compounds in food (Table 2). The advantage of NIR-HSI is the larger surface area being analyzed. In addition, it can acquire multi-constituent information and sensitive to minor chemical components. The main drawback of NIR-HSI is expensive and requires training (Jha 2010).

Table 2 Applications of NIR spectroscopy, FT-IR spectroscopy, Raman spectroscopy, and HSI technique for bioactive compound assessments of various food products
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Raman spectra are obtained in a fairly fast acquisition time (milliseconds to minutes, depending on the sample). This enables for high-spatial-resolution 2D and 3D Raman mapping with tens of thousands of points being acquired in a few hours or less (Erasmus and Comins 2018). Also, in this technique, there is the absence of interference from overlapping peaks and, e.g., aqueous solutions do not present a problem (compared to, e.g., IR spectroscopy) (Alajtal 2010). In comparison with IR spectroscopy, one of the most attractive features of Raman techniques results from its weaker water interference. Thus, Raman technique can suitably analyze samples in various states (e.g., dried, hydrated, liquid, or solid state) with less sample preparation processes, which is fundamental for fast and non-destructive determination of the chemical composition of food in situ. Also, in this technique, fluorescence effect is well circumvented in the measurements and samples retained in glass bottles or simply in the raw form can be analyzed easily (Kizil and Irudayaraj 2018). A further advantage is that a Raman spectrum covers the spectral range between 4000 and ∼ 100 cm−1, depending on how effective the Rayleigh line filtering is (Y.-S. Li and Church 2014). Conversely, the acquisition of an IR spectrum over this frequency range depends on the utilization of both mid- and far-IR spectrometers. The main drawback with conventional Raman spectroscopy is the small scattering cross-section of many materials. The details of the advantages and drawbacks of infrared and Raman spectroscopy can be found in the literature (Baeten and Dardenne 2002; Y.-S. Li and Church 2014).

Chemometric Analysis

Nowadays, the research activities in chemometrics are very intense and a large number of publications dealing with the vibrational spectroscopy focus on this field. IR, Raman, and HSI are capable of providing useful information about the various characteristics of a sample. In addition to the sample properties, some redundant information originating from instrumental noise, scattering, and environmental effects might contribute to the complexity of a spectrum. These effects can be largely reduced by applying pretreatment techniques such as smoothing, standard normal variate (SNV), multiplicative signal correction (MSC), first derivative (1st Der), and second derivative (2nd Der) (Mora-Ruiz et al. 2017; Pu et al. 2015; Tahir et al. 2016a).

Due to the large overlap and the complex nature of continuous data, sometimes, it is a challenge to find the positions of characteristic bands that represent the different components in plant foods. To avoid using irrelevant information, the selection of feature wavelengths in the vibrational spectroscopy (e.g., HSI and NIR) region is a highly effective method to develop a robust and accurate model (Table 2) (Su and Sun 2018a, b). Thus, different combinations of feature wavelengths associated with specific quality parameters are expected to be efficiently captured. Several wavelength selection procedures such as successive projection algorithm (SPA) and stepwise regression (SWR) (Y.-C. Yang et al. 2015), interval selection in partial least squares (iSPA) (Mariani et al. 2015). competitive adaptive reweighted sampling (CARS) (Ma et al. 2016), and principal component analysis (PCA) (Khulal et al. 2016) have been employed over the last couple of decades. The most common modeling techniques for quantitative analysis of volatile and bioactive compounds include partial least squares regression (PLSR), least squares-support vector machine (LS-SVM), modified linear regression (MLS), artificial neural network (ANN), synergy interval partial least square regression (Si-PLS), support vector regression (SVR), and backward interval partial least squares (bi-PLS) (Table 2). Generally, a good regression model is characterized by higher coefficients in calibration (Rc), cross-validation (Rcv), and prediction (Rp) and lower error values in calibration (RMSEC), cross-validation (RMSECV), prediction (RMSEP), standard error of prediction (SEP), and standard error of calibration (SEC).

Applications on Volatile and Bioactive Components

The under-mentioned sections summarized the recent progress of the applications of spectroscopic techniques with suitable chemometrics methods in this field (Table 2 and Table 3). Generally, the applications of these techniques can be grouped into the following categories: (1) measurement of volatile compounds, (2) measurement of individual bioactive compounds, (3) measurement of a group of bioactive compounds. HSI was mostly used for (3), and Raman and MIR for categories (2) and (3), while NIR has been applied for all categories.

Table 3 Applications of IR spectroscopy, Raman spectroscopy, and HSI for volatile compounds evaluations of various food products
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Basically, determination of phytochemical compounds and vitamins using Raman, NIR, MIR, and HSI techniques requires appropriate reference chemical assays to be performed simultaneously as spectral measurements. Spectroscopic techniques are indirect tools for measuring volatile compounds and health-related components in foods. Conventional methods such as GC-MS, HPLC, and UV-vis spectrophotometer are usually performed to obtain data about volatile and bioactive compounds (such as vitamins, phenolics, and flavonoid). Thereafter, appropriate mathematic models might be developed for the determination of concentrations of either individual compound or prediction of total antioxidant compounds based on how models were developed.

Analysis of Total Phenolic Compounds

For predicting TPC using NIR, difference feature wavelengths were extracted using difference selection techniques. For example, competitive adaptive reweighted sampling-partial least square (CARS-PLS) was used to predict TPC in tea (Ma et al. 2016). Compared with the PLS model built from the whole wavelength, CARS-PLS algorithm can reduce the prediction error and enhance the correlation coefficients (R2p) from 0.8023 to 0.8412. The TPC in honey was determined using NIR reflectance (10,000–4000 cm−1) combined with various pretreatments (such as baseline correction, SNV, MSC, and 1st Der). The results indicated that the raw data model was the most robust with a high correlation coefficient (Rp = 0.94) and a higher ratio of performance to the standard deviation (RPD ˂ 3.50) which indicated the precision of NIR using spectra without any pretreatments (Tahir et al. b). NIR has also been successfully applied to obtain non-destructive measurements of TPC in many foods such as apples, red cabbage, and honey (Table 2). Based on the abovementioned studies, it can be concluded that it is possible to accurately determine TPC in foods using NIR technology. Furthermore, as presented in Fig. 4, NIR is the most effective and widespread technique for quantitative analysis of bioactive compounds in foods.

Fig. 4
figure 4

Publication numbers of applying vibrational spectroscopy for measurements of total phenolic compound content (TPC), total flavonoid content (TFC), total anthocyanin content (TAC), total antioxidant activity (TAA), specific bioactive compound (SBC), and volatile compounds (VCs) in foods from July 2011 to January 2019

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The online application of vibration spectroscopy techniques in the determination of bioactive compounds is limited. Aleixandre-Tudo et al. (2019) investigated the feasibility of online application NIR for measurement of bioactive compounds in intact and crushed grape berries transported on a conveyor belt system online (Fig. 5). The study was conducted using commercial cellar equipment, which makes this system valid for the commercial condition. The PLS results indicated that NIR technique is a powerful tool for prediction of the bioactive compounds such as phenolic compounds in wine-like and homogenate samples. The developed model showed lower prediction errors (RMSEP = 12.3%). The authors concluded that the result of this study could be implemented in fruit sorting and bench-marking as well as for decision-making reasons, before the fermentation procedure. However, comprehensive studies with a large number of samples with different maturity stages are required to demonstrate this.

Fig. 5
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Schematic illustration of the experimental setup for the spectral data collection using a conveyor belt moving online system (Aleixandre-Tudo, Nieuwoudt et al. 2019)

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TPC in red cabbage was analyzed by NIR (10000–4000 cm−1) and ATR-FT-IR (3600–3200 cm−1) (I. R. N. de Oliveira et al. 2018). The authors applied ordered predictors selection (OPS) and genetic algorithm (GA) for feature extraction prior to developing the PLS model. The results indicated that PLS combined with the OPS algorithm had better performance (RMSECV = 44.41 mg gallic acid equivalents (GAE) 100 g−1) (RMSEP = 42.46 mg GAE L−1) with higher prediction accuracy (Rp = 0.99). In another study, NIR and FT-IR were investigated and compared based on the quantification of TPC in grape juice. Results showed that both techniques had comparable performance to determine TPC with low prediction errors. However, FT-IR showed an RMSEP (0.21 mg GAE100 mL−1) which was moderately better than that of NIR (0.37 mg GAE100 mL−1) (Caramês et al. 2017b).

FT-IR (1660 variables) and Raman (2644 variables) were also compared for quantification of TPC in Chinese rice wine. The authors compared several preprocessing techniques based on prediction results (Wu et al. 2016). The optimal preprocessing technique for FT-IR was 1st derivative with Savitzky-Golay smoothing and Raman was Savitzky-Golay smoothing. In this article, the authors also compared Si-PLS and PLS models for prediction of TPC. It was observed that Si-PLS models for both techniques presented high predictions with R2p of 0.9237 for FT-IR and R2p of 0.9064 for Raman as compared to PLS model with R2p of 0.9064 for Raman and R2p of 0.8918 for FT-IR. The results of this article demonstrated that the accuracy of models based on the Raman data was superior to those based on the FT-IR data. FT-Raman of honey showed very high accuracies for TPC (Rp = 99.9, RMSEP = 1.44 mg 100−1g) when PLS and preprocessing such as straight-line subtraction and MSC were employed (Anjos et al. 2018).

Recently, a multispectral technique coupled with BPNN modeling for quantitative analysis of carotenoid and TPC in tomato samples was investigated (Liu et al. 2015a). Results demonstrated the feasibility of multispectral imaging application for accurate and rapid evaluation of bioactive compounds such as lycopene (R2p = 0.938, RMSEP = 2.29 mg kg−1) and TPC (R2p = 0.965, RMSEP = 0.31 mg GAE 100 g−1) distribution in tomato fruits.

Analysis of Total Flavonoids

NIR technique was evaluated toward the determination of TFC propolis, karkade tea, honey, apple, mint tea, and coffee. It was observed that PLSR was the most widespread method for measuring TFC in various foods. The popular models for detections of TFC were MPLS, PLS, and LS-SVM combined with a wide range of spectra, close to full-wavelength models. The correlation coefficients in raw propolis were R2p = 0.96 (Revilla et al. 2017), goji berry Rp = 0.9075 (Tingting et al. 2016), Karkade tea Rp = 0.83 (Tahir et al. 2016a, b, c), and honey Rp = 0.95 (Tahir et al. 2016a, b, c). Revilla et al. (2017) and Betances-Salcedo et al. (2017) successfully applied NIR equipped with remote reflectance fiber-optic probe to ground up propolis for determining the TFC raw propolis. The result showed that NIR technology with a fiber-optic probe could be used as an alternative to the chemical methods.

ATR-FT-IR was also evaluated for prediction of TFC in Moscatel dessert wines (Silva et al. 2014). PLS model on the fingerprint zones (1800–900 cm−1) yielded higher prediction (Rp = 0.811) with a lower error of prediction (REP = 0.20%).

Analysis of Anthocyanins

NIR was also evaluated for determination of TAC in various foods. For measuring of TAC, Tahir and co-authors applied NIR together with PLS to predict the TAC in karkade tea (Rp = 0.91) (Tahir et al. 2016b). In similar research, Viegas et al. (2016) used NIR combined with PLS to quantify TAC in Wax Jambu fruit. The authors compared the performance of various pretreatments (such as smoothing, MSC, 1st Der, and 2nd Der). PLS combined with 2nd derivative showed the highest correlation (Rp = 0.98) and RPD (5.19) with the lowest prediction error (RMSEP = 9.0 mg L−1). More recently, NIR was used to evaluate TAC in pomegranate (Arendse et al. 2017). In this study, two NIR acquisition approaches, i.e., integrating sphere (1064–1333, 1640–1835 nm) and emission head (1064–1333, 1640–1835 nm), were compared. Among the two NIR acquisition approaches, emission head (EH) showed the TAC (R2p = 70.50) with lower prediction error (RMSEP = 0.13 g GAE L−1). NIR was applied to measure the TAC in flowering tea (i.e., Camellia japonica, Hibiscus sabdariffa, Rosa chinensis, Rosa rugosa, Dianthus caryophyllus, and Myosotis sylvatica) (Xiaowei et al. 2014). In this article, interval PLS (iPLS), a colony optimization interval partial least squares (ACO-iPLS), and genetic algorithm interval partial least squares (GA-iPLS) were applied on the data with superiority given to ACO-iPLS, as acquired in the characteristic regions for TAC (4590–4783, 5770–5963 cm−1). Based on the selected region, ACO-iPLS yielded higher predictions (Rp = 0.9524) with lower prediction error (RMSEP = 0.12 mg/g). Similarly, the possibility of application of NIR coupled with PLS algorithm to quantitatively determine the TAC in açaí (Euterpe oleracea Mart.) and palmitero-juçara (Euterpe edulis Mart.) fruits was investigated (Inácio et al. 2013). The iSPA-PLS model using smoothing preprocessing with a window of 5 points revealed better prediction than the model built with spectra pretreated by 1st Der combined with 2nd Der, demonstrating the effectiveness (R2p = 0.90, RMSEP = 9.35 g kg−1) of the smoothing (with 5-point method) (Mariani et al. 2015). Based on NIR spectra together with the PLS model containing 600 variables, TAC in Jaboticaba fruit could be successfully quantified (R2p = 0.89). Other authors found that the NIR spectroscopy could be employed to determine TAC in black Goji berry accurately with Si-PLS model estimation (Rp = 0.899, RMSEP = 0.60 mg/g) (Yahui et al. 2017). PLS with 2nd Der presented higher prediction results (R2p = 0.86).

In another study, NIR (10,000–8450 cm−1) and FT-MIR (747.42–829.11 cm−1) were used for quantifying TAC in grape juice (Caramês et al. 2017b). In the case of the PLS model, both spectroscopies had a similar satisfactory performance to predict TAC presenting low RMSEP = 4.22 mg 100 g−1 for FT-MIR and RMSEP = 4.44 mg 100 g−1 for NIR. Additionally, the coefficient of determination was relatively equal to R2c = 0.81 for FT-MIR and R2c 0.84 for NIR.

FT-IR (4000–650 cm−1) coupled with the PLS model was used to predict TAC in red and rose wine (Canan and Banu 2017). Several spectral preprocessing techniques were applied before the PLS model analysis of FT-IR spectra. In some FT-IR study, the cross-validation of the TAC model indicated a weak performance (Andrianjaka-Camps et al. 2015). This result could be attributed to the small number of raspberries puree used for developing PLS model. An analysis with a larger number of samples may be considered to enhance the performance of the model.

The potential of HSI technique to quantify TAC in foods has been studied. In this regard, the feasibility of HSI to measure TAC in wine grapes was reported (Chen et al. 2015). The prediction based on SVR with 60 latent values obtained from PLS with smoothed spectra had the highest prediction accuracy (R2p = 0.9414, RMSEP = 0.0046 mg g−1) as compared to PLS model (R2p = 0.8407, RMSEP = 0.0129 mg g−1). In another article, the feasibility of HSI for screening the nonacylated and total anthocyanins on the intact red grape during ripening was described (Hernández-Hierro et al. 2013). The MPLS models with fingerprint zones (950–1650 nm) demonstrated the potential of measuring both nonacylated (R2p = 0.86, SECV = 1.70 mg g–1) and total anthocyanins (R2p = 0.86, SECV = 2.41 mg g–1). However, the high correlation achieved between the compounds revealed that it is not possible to verify whether the results of HSI technique for measuring the composition nonacylated anthocyanins were attributable to their real absorbance or the relationship among nonacylated anthocyanins and total anthocyanins (Hernández-Hierro et al. 2013). Again, HSI was successfully employed for quantitative determination (R2p = 0.92) of TAC in lychee pericarp during storage of samples (Y.-C. Yang et al. 2015). Wavelength selection technique, such as successive projection algorithm (SPA) and stepwise regression (SWR), was applied to the spectral data. Based on selected wavelengths by SWR (11 bands) and SAP (9 bands), RBF-SVR model attained worse results for SPA-RBF-SVR (R2P = 0.672, RMSEP = 0.93 mg g−1) and SWR-RBF-SVR (R2p = 0.712, RMSEP = 0.84 mg g−1) as compared with RBF-SVR based on full-wavelength models (R2p = 0.916, RMSEP = 0.51 mg g−1). This technique coupled with LS-SVM also was efficiently (R2p = 0.959, RMSE = 0.146 mg g−1) employed for quantitative analysis of TAC in mulberry fruit (Huang et al. 2017). Similar performance (R2p = 0.866, RMSECV = 0.32 mg g−1) was observed in the study conducted by Y. Liu et al. (2017). HSI technique was also successfully employed for the determination of TAC in wine grape with R2p of 0.907 (Gomes et al. 2017).

Analysis of Individual Phenolic Compounds

Non-destructive measurement of individual phenolic, flavonoid, and anthocyanin has also been developed and the evidence can be found in a number of the articles presented in this work (Table 2).

NIR spectroscopy combined with chemometric was applied to analyze nine individual catechin, total catechin, and gallic acid in green tea leaves. MPLS models presented high reproducibility and sensitivity for detection of catechins in the leaves of green tea, simultaneously improving its rapid and cost-effective features (Lee et al. 2014). Cross-validation models showed good correlations (R2p = 0.58–0.97) between reference measurements and NIR estimates. Inarejos-García et al. (2013) investigated the use of NIR (12,500–4000 cm−1) combined with chemometric for the determination of several phenolic compounds in olive oil. Although most of the compounds showed satisfactory results, hydroxytyrosol and tyrosols showed very low correlations, indicating that the NIR technique could not be used for the quantitative analysis of these minor compounds in olive oil (Table 2). The application of NIR techniques combined with chemometrics for assessing individual phenolic acid and flavonoid in food was comprehensively demonstrated (Table 2). A comparison between the capability of NIR and HPLC methods suggested an overall agreement between these two methodologies. However, NIR has some advantages compared to HPLC. For example, NIR is cost-effective and time-saving, and it enables a substantial reduction in chemicals with minimal sample preparation. Aleixandre-Tudo et al. (2018) employed FT-NIR, FT-MIR, and FT-IR to monitor the main individual phenolic compounds during wine fermentation. FT-NIR appeared to be the most accurate procedure to predict the phenolic content. Although slightly less accurate models were established, ATR-MIR and FT-IR can also be applied for the prediction of the majority of phenolic compounds. While the abovementioned publications demonstrated the potential of NIR for measurement of specific bioactive compounds, some authors recommended that its reliability needs to be further modernized, expanded, and enhanced with more samples (Ferrer-Gallego et al. 2011; Lee et al. 2014). Generally, FT-NIR appears to be the most accurate to quantify the phenolic compounds as compared with the other techniques.

FT-IR was employed for the prediction of various phenolic acid in coffee beans (Table 2). Results provided evidence of excellent performance for measurement of nine phenolic compounds (R2p ˂ 0.92) for various chlorogenic acid isomers in coffee beans (Liang et al. 2016). In this article, for the first time, FT-IR was applied to the pure standard of chlorogenic acid isomers in order to extract the fingerprint regions (1700–1500, 1300–800 cm–1) to build PLS models and quantify individual chlorogenic acid isomer concentration in coffee beans. FT-IR also was employed for screening the main proanthocyanidins in chocolate (Hu et al. 2016). Chocolates and the (+)-catechin standard were analyzed in the range of 4000–550 cm−1 and fingerprinting region (1800–700 cm−1) was selected based on the targeted functional groups that appeared in (+)-catechin region. The obtained PLS results (R2p ˂ 0.72) recommended the possible use of FT-IR for applications in the food industry and commercial laboratories. Another FT-IR application was established to predict individual anthocyanin in red grape must (Rasines-Perea et al. 2015). However, the results showed the unsuitability of this technique for its application in red grape musts (R2p = 0.46–00.66). Some FT-IR investigations showed worse results and some components were not detected (for example, vanillic, cinnamic, caffeic, and p-coumaric acids) when used for quantification of low-concentration compounds (Uncu and Ozen 2015). A comparative analysis was carried out in order to validate the performance of FT-IR and Raman techniques, as well as their combination, in the determination of individual phenolic compounds in honey. Generally, FT-IR and Raman methodologies presented good results (R2p ˂ 0.99) (Tahir et al. 2017). In this study, only samples of Sudanese honey varieties were presented for examination. Therefore, a further study involving large samples from different countries is needed before such methods can be adopted by food, pharmaceutical, and apiculture industries with confidence. Several anthocyanin concentrations of wine that were analyzed by MIR spectroscopy showed acceptable prediction results (R2val = 0.38–0.81), suggesting that this technique could be used to screen the changes of these compounds at different stages of the production process and mainly aging (Sen et al. 2016). BP-ANN models showed high performance (R2p = 0.9679), and the average relative error was 0.80%, which revealed the accuracy of the projected method and provided a theoretical basis for the applications of HSI in non-destructive determination for interior quality of soybean (Kezhu et al. 2014).

Analysis of Vitamins

Many studies have focused on the analysis of raw materials to investigate their potential antioxidant activity over the past three decades. However, in most of these studies, chlorophyll had not been involved in the trials regardless of the fact that it is the major pigment in nature (Lanfer-Marquez et al. 2005). Blanco-Díaz et al. (2014) employed NIR combined with MLS model to predict the chlorophylls in summer squash and reached good prediction results (R2p), where chlorophyll-a was 0.66 and chlorophyll-b was 0.79. Furthermore, the feasibility of NIR technique to quantify lycopene in carrot was studied (Ding et al. 2016). The results indicated better performance (Rp = 0.939), when radial basis function neural networks (RBF-NN) was applied to the NIR data. Nordey et al. (2014) also conducted a feasibility study on the application of portable NIR spectroscopy (350 to 2500 nm) coupled with PLS model to measure various bioactive components (i.e., chlorophyll-a, chlorophyll-b, carotenoid) in mango peel surface. The PLS models appeared to be very promising, particularly the ones achieved for the measurement of chlorophyll-a and chlorophyll-b (R2p = 0.99). NIR data of summer squash fruit flesh and skin were used to predict individual and total carotenoids (Martínez-Valdivieso et al. 2014). This result showed that NIR had good potential for the high-performance monitoring of total carotenoids (R2p = 0.95) and in specific components such as lutein (R2p = 0.96) and β-carotene (R2p = 0.81), indicating that NIR is a promising tool for the non-destructive determination of carotenoid profiles. Also, the NIR technique with PLS modeling was used for the prediction of carotenoid in honey and the results were very promising (Rp = 0.96) (Tahir et al. 2016b). Furthermore, the NIR technique was applied for the prediction of individual carotenoid in tomato juice. Results revealed the satisfactory performance of the projected method (R2cv ˂ 0.75), for the analysis of cis-lycopene diepoxide, lycoxanthin, zeaxanthin, and β-carotene in tomato juice sample (Deak et al. 2015). NIR technique was also used to quantify β-carotene in mango fruit and high accuracy (R2p = 0.84, SECV = 16.55 retinol equivalents 100 g−1) was achieved based on the MLR model (Rungpichayapichet et al. 2015).

Tocopherol such as α-tocopherol, β-tocopherol, δ-tocopherol, and γ-tocopherol and γ-tocotrienol is a form of vitamin E and natural antioxidant with regulatory cellular and molecular roles (Kim and Cho 2015; Žilić et al. 2016). NIR technique was also successfully (R2p = 0.865) used for the determination of vitamin E in quinoa (Moncada et al. 2013). NIR spectroscopy showed a high potential to analyze several tocopherols in olive oil (Cayuela and García 2017). These authors compared the prediction results attained by NIR and Vis-NIR range. Results achieved by the two acquisitions were promising, particularly for α-tocopherol, γ-tocopherol, and total tocopherols (Rp = 0.91) while β-tocopherol showed the worse result (Rp = 0.41). Similarly, Inarejos-García et al. (2013) showed that FT-NIR had lower (R = 0.14) prediction results of β-tocopherol in olive oil.

Vitamin C (l-ascorbic acid) is an essential water-soluble antioxidant originating from citrus and other fruits. It may be a factor in cardiovascular, immune cell development, and iron use function in human (Gallie 2013). Vibrational spectroscopies were previously used for prediction of water-soluble vitamins (Blanco et al. 1993; Wojciechowski et al. 1998; H. Yang and Irudayaraj 2002). However, most of these studies were performed in pharmaceutical products. Most recently, NIR seems to be a promising technique for quantification of ascorbic acid in foods. In many studies, the simple PLS and MPLS models were feasible in the quantification of ascorbic acid in cashew apple, guava nectar, and guava pulp using NIR spectroscopy, producing high prediction (R2p about 0.85 for guava products and 0.84 for cashew fruit) (Alamar et al. 2016; Caramês et al. 2017a). According to Andrianjaka-Camps et al. (2015), the least square support vector machine (LS-SVM) showed the feasibility of quantifying ascorbic acid in apple using NIR spectroscopy. The results of the LS-SVM model presented an accurate estimation of ascorbic acid in apple (R2p = 0.80). A similar result of LS-SVM (Rp = 0.83) was observed when NIR was used for the prediction of ascorbic in orange samples (Liu et al. 2015b). Additionally, this technique was applied in the determination of ascorbic acid in summer squash with R2p of 0.86 (Blanco-Díaz et al. 2014) and passion fruit with Rp of 0.663 (Maniwara et al. 2014). The authors attributed the lower calibration and prediction results to the low concentration of ascorbic acid content and physical characteristics of passion fruits such as a waxy pericarp and thick mesocarp. A similar result (R2p = 0.61) was observed in cabbage (Kramchote et al. 2014); this result was expected since the ascorbic acid is not the main compound in cabbage. Beghi et al. (2013) also investigated ascorbic acid in Stark Red Delicious and Golden Delicious apple samples by portable NIR technique (450–980 nm) combined with PLS. However, the results revealed that NIR spectroscopy was not effective for evaluation of ascorbic acid in apples. The lower prediction results (R2cv = 0.40) in Stark Red Delicious could be justified by a very low concentration of this component in this cultivar. FT-NIR, FT-MIR, and FT-IR were used comparatively for prediction of ascorbic acid in pomegranate juice (Arendse et al. 2018). The result of FT-NIR technique was very promising for quantifying ascorbic acid (R2p = 0.709). Similar findings were achieved from the application of NIR by integrating sphere as an acquisition method, which enabled the prediction of ascorbic acid in pomegranate with high accuracy as presented in Table 2. Quantification of ascorbic acid concentration in raspberry puree using FT-IR was conducted by Andrianjaka-Camps et al. (2015), who found that Si-PLS model was far more accurate (R2p = 0.94) in the determination of ascorbic than those reported using NIR spectroscopy. A comparative study was conducted in order to measure the feasibility of NIR and MIR techniques in the determination of carotenoids in passion fruit. Overall, NIR slightly showed high performance (R2cv = 0.56, RMSECV = 0.04 mg g−1) than MIR spectroscopy (R2cv = 0.772, RMSECV = 0.045. However, both techniques presented low prediction results and the authors attributed that to the low concentration of carotenoid in passion fruit (G. A. de Oliveira et al. 2014).

Carotenoid is considered to be an effective bioactive component and may have protected cells against oxidative damage and, potentially, reduce the risk of cancer particularly prostate cancer (Martí et al. 2016). Indeed, lycopene is the main carotenoid determined in tomatoes, constituting about 80–90% of the total pigment contents (Rizk et al. 2014). In the case of Raman spectroscopy, different Raman zones (i.e., 200–2000, 300–1900, 900–1650, 1100–1600 cm−1) with PLS were used to quantify lycopene in tomato fruits (Fu et al. 2016). However, all PLS models failed to quantify lycopene content accurately with the lower prediction (Rp = 0.57) and high prediction error (SEP = 14.2 μg g−1). The authors, therefore, attributed the poor results to heterogeneous samples and poor laser power (Fu et al. 2016). In another study, Krähmer et al. (2016) compared the effect of various pretreatment methods on the different FT-Raman spectral range to quantify individual and total carotenoids in carrots with high predictions (R2p ˂ 0.80). Similar prediction result (R2p about 0.85) was observed from the application of Raman for determination of total carotenoid in the carrot (Lawaetz et al. 2016). Recently, Raman spectroscopy together with PLS model for carotenoid determination in processed sweet potato was studied (Sebben et al. 2018). The prediction results for the sample treated with hot air was R2p = 0.90 and by microwave was R2p = 0.88. Although Raman technique showed great potential for quantitative analysis of this compound, satisfactory reproducibility of Raman predictions relies on the features of the sample and its condition after processing. For instances, sample dried using microwave showed higher prediction, although RMSEC of these groups was higher than samples of hot air samples. From the abovementioned studies, food and pharmaceutical industries can practically make use of these vibrational spectroscopies which are not laborious but cost-efficient in the analysis of vitamins.

Analysis of Volatile Compounds

Good functional food must provide both nutrient-specific health-promoting functionality and satisfactory sensory characteristic to meet consumer requirements (Sun-Waterhouse and Wadhwa 2013). Recently, the NIR technique was investigated for the determination of the aroma compounds in white wines during different stages of storage (Genisheva et al. 2018). For extraction of the useful information, PCA was applied and PLS models were used for the quantification of the volatile compounds. The derived models using NIR data and PLS presented high predictions (R2p ≤ 0.95) and can be used for other types of wine or even to the entire food field. Musty taint odor is unpleasant organoleptic defects in wines and it is a serious problem in winemaking worldwide (Apostolou et al. 2014). In this regard, the NIR technique was used to screen haloanisoles and halophenols accountable for musty taint defect in barrel aged red wines. From the results, the prediction of haloanisoles and halophenols using GC-MS and NIR together with PLS model was good (R2p about 0.80). The authors concluded that these compounds can be monitored rapidly and simply by this technique (Garde-Cerdán et al. 2012). However, the prediction errors achieved were below the sensory threshold standards described for these compounds, making this method unsuitable (dos Santos et al. 2017). The major aroma compounds of lavandin and lavender oils were investigated using NIR spectroscopy (Table 3). Apparently, the correlation coefficients (R2p = 0.97) obtained from the PLS regression were satisfactory (Lafhal et al. 2016). In addition, the results indicated that this technique might be applied to other oils commonly used in medicine and other fields. Furthermore, NIR was used for the determination of low concentrations of aroma compounds in olive oil (Inarejos-García et al. 2013). However, poor correlations were observed for some components such as E-2-hexenal (Rp = 0.42) and C6 aldehydes (Rp = 0.43).

NIR with remote fiber-optic reflectance probe to analyze volatile compounds in cheeses (González-Martín et al. 2014). The calibration models developed using 67–72 samples of cheese had a correlation coefficient (R2) between 0.600 for the 3-methyl-1-butanol and 0.903 for the 2-nonanone. In this study, the robustness of the MPLS models was confirmed by applying it to 20 new samples of different compositions and ripening times, which did not belong to the calibration set. The results of that study showed that NIR was similar to those of the purge-and-trap gas chromatography-mass spectrometry (Table 3). Measurement of aromatic plant terpenoid content is an important issue in many aspects. Results from Ercioglu et al. (2018) summarized in Table 3 showed that NIR can be used to predict terpenoid profile in plants with high coefficient of determination (R2) values in the range of 0.953–0.997. These results demonstrated that NIR could provide worthy information with respect to aromatic plant authentication, impurity, and chemotypes in consideration of a variety of terpenoid compounds. Spectroscopic investigations have been established for FT-NIR to monitor the off-flavors in olive oil (Inarejos-García et al. 2013). The results showed that FT-NIR is a powerful technique that allows rapid monitoring of cornicabra virgin olive oil samples to determine their volatile profile and thus their quality and commercial grade. The volatile compounds were well correlated with the FT-NIR spectra in the case of C6 alcohols (r = 0.69–0.80), accountable for the green sensory notes in high-quality cornicabra virgin olive oils. Although the FT-NIR showed promising results, further research is required by sampling various varieties of olive oils from different geographical areas before applying FT-NIR for screening the flavor of olive oils in the food industry. A similar trend was found when the NIR technique was used to monitor the alcohol strength during fermentation of apple wine (Peng et al. 2016).

Most recently, a comparative study was conducted in order to assess the potential of using NIR, MIR, and Raman techniques for determination of alcoholic strength in wine (Teixeira dos Santos et al. 2018). Overall, MIR technique revealed an excellent performance in the establishment of calibration models for the determination of alcoholic strength in white wine (Table 3).

FT-Raman was used to predict natural pesticides (e.g., antifungal) in carrot including falcarinol, falcarindiol, and 3-O-acetylfalcarindiol (Krähmer et al. 2016). The results showed that FT-Raman could be used for quantitative analysis of falcarindiol while falcarinol and 3-O-acetylfalcarindiol models can be used for screening purpose. Although the NIR and Raman showed great potential for prediction of volatile compounds in the oils, wine, cheese, and aromatic plant, future investigations are required to demonstrate the feasibility of their applications in industrial settings.

Technical Challenges and Future Trends

Advances in IR, Raman, and HSI techniques have presented enormous opportunities for the research community, pharmaceutical, and food industries to establish rapid, non-destructive analytical, and environmental-friendly methods for food bioactive materials and aroma inspection. Based on these no-preparation and chemical-free assessment methodologies, the analysis time can be reduced and the errors arising during the extraction of the bioactive active or aroma compounds can also be decreased. Concerning all of the environmentally friendly technologies reviewed and discussed in this article, there are still some issues that impede the adaptation of the latest scientific research achievement obtained in a laboratory level to industrial implementations. These include (a) requirement for specific mathematics knowledge to perform this task; (b) building appropriate preprocessing method to reduce the influence of physical impacts on raw spectral data; (c) establishing state-of-the-art spectral analysis procedures capable of filtering useless information; (d) manipulating proper chemometric approaches for enhancing the model accuracy for simultaneous implementations; and (e) reducing the price of the technologies. Then, the chemical properties of functional groups of foods presented in the spectra will be used for quantitative analysis of biologically active materials and aroma compounds effectively. Thus, the food and nutraceutical industries can practically take advantage of the opportunity to implement these non-destructive approaches without laborious and inefficient chemical assays and thus prominently improve the monitoring quality and biologically active material in foods. A previous study (Aleixandre-Tudo et al. 2019) showed NIR had achieved success in measuring of phenolic compound in whole and crushed berries that are transported on a moving conveyor belt. Thus, future work should focus on enhancing the efficiency of previously presented algorithms, which may increase the prediction accuracy of the model. To some extent, the application of vibrational spectroscopies in volatile compounds analysis is still not at a mature stage and a limited number of studies have been published. Thus, further study is expected to establish useful algorithms existing in other fields for their applications in aromatic compound detection.

Conclusions

The recent publications in the field of vibrational spectroscopy indicated that NIR, FT-IR, and Raman could provide accurate measurement for bioactive and volatile compounds of various foods. Obviously, not only is the application of vibrational spectroscopy to analyze biological active materials and aromatic compounds limited to laboratory application but also, in some cases, online measurement has been reported. However, the frequencies of application of vibrational spectroscopies to determine biological active material and aroma profile were varied greatly. Figure 4 indicated the number of published articles on applying various vibrational spectroscopies for determinations of various bioactive and volatile compounds in food during the period of July 2011 to January 2019. It is obvious from this figure that NIR was more used for determination of bioactive compounds and volatile compounds followed by FT-IR techniques. Although Raman and HSI techniques were fewer applications in this field, an increasing number of research works in the past few years have demonstrated their application for measurement of bioactive compounds in food. This article shows that infrared spectroscopy, Raman, and HSI techniques are now convenient approaches to evaluate the maturity and antioxidants, and able to characterize raw and processed foods, and monitor products during storage and processing.