Thermal Stability and Molecular Microstructure of Heat-Induced Cereal

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Thermal Stability and Molecular Microstructure of HeatInduced Cereal Grains, Revealed with Raman Molecular Microspectroscopy and Differential Scanning Calorimetry Md. Majibur R. Khan, and Peiqiang Yu J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/jf401306z • Publication Date (Web): 31 May 2013 Downloaded from http://pubs.acs.org on June 12, 2013

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Journal of Agricultural and Food Chemistry

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Thermal Stability and Molecular Microstructure of Heat-

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Induced Cereal Grains, Revealed with Raman Molecular

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Microspectroscopy and Differential Scanning Calorimetry

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MD. MAJIBUR R. KHAN1,3, PEIQIANG YU1,2*

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Department of Animal and Poultry Science, College of Agriculture and Bioresources, University of Saskatchewan, Saskatoon, SK, S7N 5A8, Canada

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Tianjin Agricultural University, 22 Jinjin Road, Tianjin, China

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* Corresponding Authors:

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Peiqiang Yu, Ph.D.

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Professor

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Chair, Ministry of Agriculture Strategic Research Program

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College of Agriculture and Bioresources

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University of Saskatchewan, Canada

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Tel:

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E-mail: [email protected]

+ 1 306 966 4132;

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Current address: Department of Mechanical and Manufacturing Engineering, University of Manitoba, Winnipeg, MB, R3T 5V6, Canada

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Abstract:

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The objectives of the present study were to use Raman molecular microspectroscopy and

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Differential scanning calorimetry to reveal molecular thermal stability and thermal degradation

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behavior of heat-induced cereal grains and reveal the molecular chemistry of the protein

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structures of cereal grain tissues affected by heat-processing, and to quantify the protein

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secondary structures using multicomponent peak modelling Gaussian and Lorentzian methods.

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Hierarchical cluster analysis (CLA) and principal components analysis (PCA) were also

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conducted to identify molecular differences in the Raman spectra. Three cereal grain seeds,

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wheat, triticale, and corn were used as the model for feed protein in the experiment. The

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specimens were autoclaved (moist heating) and dry heated (roasted) at 121°C for 80 min,

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respectively. Raman spectroscopy results revealed that there are marked differences in the

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secondary structures of the proteins subjected to various heating treatments of different cereals.

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The sensitivity of cereals to moist heating was much higher than the sensitivity to dry heating.

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The multivariate analyses (CLA and PCA) shown that heat treatment were significantly isolated

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between the different Raman raw spectra. The DSC study revealed that the thermal degradation

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behavior of cereals was significantly changed after moist and dry heat treatments. The position

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of the major endothermic peak of dry heated cereals shifted toward higher temperature, from

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131.7 to 134.0 °C, suggesting the high thermal stability of dry heated cereals. In contrast, the

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endothermic peak position was slightly decreased to 132.1 °C in case of moist autoclaved

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heating. The digestive behaviour and nutritive value of rumen-undegradable protein in animals

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may be related to the changes of the protein secondary molecular structure and thermal stability

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of the cereal grain materials which is attributed by Raman microspectroscopy and DSC

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endotherm profiles. 2 ACS Paragon Plus Environment

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KEYWORDS: Thermal stability; thermal degradation; heat treatment; protein fine

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structure

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INTRODUCTION

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The nutritive value, degradation characteristics, utilization and availability of protein are

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not only determined by the nutrient or chemical composition but also affected by intrinsic

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chemical structures such as protein secondary structures and biological component matrix (1-4).

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Therefore, an understanding of the molecular structure of the whole protein is often vital to

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understanding its digestive behavior, nutritive quality, utilization, and availability in animals (5-

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7). Protein secondary structures include mainly the α-helix and β-sheet, with small numbers of β-

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turns and random coils. The influence of such structures, α-helix and β-sheet ratio on the protein

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quality, utilization, availability and digestive behavior were important in feed evaluation (2,3).

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The protein structure α-helix to β-sheet ratio affects total intestinally absorbed protein supply

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(protein DVE value) and degraded protein balance (protein OEB value) (8-10).

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Protein functionality is closely related to its conformational state, which in turn is

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completely influenced by processing conditions. Temperature is one of the most important

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factors in a process performance, since high temperature causes protein unfolding and lost of

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functionality. Recent published reviews show that the effects of heat processing on protein

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nutritive value and performance in animals are ambiguous (11). Part of reason is that heating

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conditions inside a feed may not be optimal, resulting in the feed being either under-heated or

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overheated. In ruminants, heat processing has been used to improve the utilization and 3 ACS Paragon Plus Environment


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availability of nutrients (12-14) and inactivate anti-nutritional factors (15). However, studies on

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protein secondary structures in relation to nutritive value and digestive behaviors of protein in

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animals are extremely rare. Recently, Samadi and Yu (16) studied the dry and moist heating-

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induced changes in protein molecular structure, protein subfraction, and nutrient profiles in

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soybeans. They found that heat treatments changed the protein molecular structure. Both dry and

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moist heating increased the amide I to amide II ratio. The sensitivity of soybean seeds to moist

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heating was much higher than the sensitivity to dry heating in terms of the structure and nutrient

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profile changes. The autoclaved moist heating at 120°C for different treatment times greatly

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influenced the molecular structure of vimy flaxseed protein (7). It has been showed that heating

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changed the chemical profile and protein sub-fractions, generally decreased ruminal

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degradability of protein and increased potential nutrient supply. The protein secondary structure

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α-helix to β-sheet ratio had positive correlation with total intestinally absorbed protein supply

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and negative correlation with degraded protein balance.

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Raman microspectroscopy is a powerful technique to study the microstructural

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characterization of biopolymer which provides information on the micro-environment and

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chemistry of protein side chains as well as on the conformation of the protein polypeptide

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backbone (17-21). Changes in Raman bands can give information on the secondary structures of

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proteins, particularly in the amide I and amide II region (1580-1700 cm-1), which are due to

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contributions from C=O stretching vibration of the amide group, coupled with the in-plane N-H

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bending and C-N stretching vibration (17-19). However, to our knowledge, there have been no

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Raman spectroscopic studies performed on animal feed materials processing under different

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heating conditions.

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Differential scanning calorimetry (DSC) has been widely used to study protein

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thermodynamics, folding, and interactions (22-24). It has also been used to characterize protein 4 ACS Paragon Plus Environment

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thermal stability, overall conformation, and domain folding integrity during formulation and

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product development by the biopharmaceutical industry (25-27). It is very rare to utilize DSC

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technique to evaluate the molecular structural features of animal feed materials related to its

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nutritive quality, utilization, and availability in animals.

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In summary, the structural changes occurring in animal feed materials such as different

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cereals grains: wheat, triticale and corns during heat-induced processing are not clearly

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understood. A better understanding of the structural changes of cereal proteins and carbohydrates

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occurring in feed products induced by thermal treatments could be helpful to elucidate their role

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in the protein matrix structure and for the development of new healthy animal feed products. The

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objective of this study were to reveal molecular chemistry of protein structures of cereal grain

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affected by heat processing at molecular level, using the Raman molecular spectroscopy and

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DSC techniques as a novel approach, and to quantify protein secondary structural data in relation

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to other studies on protein digestive behaviors of different cultivars.

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MATERIAL AND METHODS

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Experimental Materials

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Three different types of cereal grains: wheat (CDC Go, Kermen Research Farm,

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Saskatoon, SK, Canada, harvested at 2007), triticale (DeKalb RR DKC, Wright County, MN,

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USA, harvest at 2011), and corn (AC Ultima, Lethbridge, AB, Canada, harvested at 2011 ) were

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used in the experiments. The cereals were provided by Crop Development Center (University of

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Saskatchewan, Canada) and Lethbridge Research Center, Agriculture and Agri-Food of Canada,

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Canada.



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Heat Treatment and Processing

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A 2-kg samples of each cereal was heated by moist heating (autoclaving) and dry heating

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(Amsco Eagle SG-3031; Steris Corp., Mentor, OH) for 80 min at 121 °C. The temperature and

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duration was chosen based on the preliminary study of Mustafa et al. (2003) (28). The treatment

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was done in two batches as replication. Control samples were kept untreated. Heated samples

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were subsequently cooled at room temperature (20–22 °C) and then placed in the refrigerator to

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minimize sticking and clumping while grinding. The samples were ground using a Retsch SM

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2000 (Retsch Inc., Newtown, PA) fitted with a 2-mm screen. Samples were fed slowly into the

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grinder to further prevent sticking and clumping during the grinding process and to ensure the

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seed was cracked open and not “extruded” through the screen.

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Raman Molecular Spectroscopy

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Raman spectroscopy measurements were carried out using a Renishaw InVia Raman

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microscope equipped with a solid state laser diode (Renishaw) operating at 785 nm and a

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1200 lines/mm grating. The microscope was focused onto the sample using a Leica 20× N PLAN

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objective lens (NA = 0.40) and backscattered Raman signals were collected with a Peltier cooled

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CCD detector. The instrument was operated in the line focus confocal mode at a 32 s detector

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exposure time with 32 spectra accumulations. The laser power was set at 0.1% (> 300 mW

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measured at the output aperture of laser). The instrument was calibrated using an internal Si

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sample, which was measured at 520 cm−1. The spectra were recorded in the range of 400–

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3600 cm−1. The Raman spectral data of each area were analyzed using OMINIC 7.2 (Spectra-

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Tech, Madison, WI, USA) software. Chemical functional groups were identified according to

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previous literature (29-31). To calculate the secondary structure components, the amide I region

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(1600−1700 cm-1) was truncated and deconvoluted using a nonlinear least-squares curve-fitting 6 ACS Paragon Plus Environment

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subroutine, which included mixed Gaussian and Lorenztian components. The tentative peak

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assignments of deconvoluted amide I components were performed according to published

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literature (32) as shown in Table 1. The percentage of each secondary structure component, that

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is, α-helix, β-sheet, β-turns, and random coil, was determined by the following equation (33):

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Percentage of sec ondary structure =

area of sec ondary structure ×100 area of amide I band

(1)

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Differential Scanning Calorimetry

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Differential scanning calorimetry (DSC) measurement was performed by a DSC Q2000

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modules instrumental (Q Series™ Thermal Analysis, TA Instruments, USA) at a heating rate of

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10 °C min-1 under N2 gas atmosphere. The endothermic transition peak (Td) related to thermal

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decomposition of cereals was determined as the peak value of the endothermal phenomenon in

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the DSC curve. The enthalpy of fusion (∆Hf) which indicates how much energy is needed to melt

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accompanying thermal decomposition for the cereals from their crystalline state was determined

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from the total area of DSC endotherm.

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Statistical Analysis The statistical analysis of Raman spectra and DSC analysis were performed using the MIXED procedure of SAS software (34). The model used for analysis was as follows:

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Yij = µ + Fi + Pj + Fi × Pj + eijk

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Where, Yij is an observation on the dependent variable ij; µ is the population mean for the

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variable; Fi is the effect of cereal seed variety as a fixed effect, Pj is the effect of heat treatment

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as a fixed effect; Fi x Pj is the interaction between cereal grains and processing methods and eijk

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is the random error associated with the observation ijk. Processing batches as replication. When a 7 ACS Paragon Plus Environment


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significant difference was detected (P < 0.05), means were separated using the Fisher’s Protected

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least significant difference test.

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Multivariate Analysis of Protein Spectra

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The multivariate methods of data analysis were used to classify spectral groups by

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applying the whole spectral information. The multivariate analysis included agglomerative

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hierarchical cluster analysis (CLA), using Wards’ algorithm method without prior to

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parameterization, and principal component analysis (PCA), which was performed using Statistica

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software 8.0 (StatSoft Inc., Tulsa, OK, USA). For the purpose and objectives of this study

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statistical comparisons were performed within grain typeS (wheat, triticale or corn) and heating

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conditions (control, dry and autoclaved), and results were presented under the same theme.

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RESULTS AND DISCUSSION

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Thermal Degradation of Cereal Grain Materials Affected by Heating Processing

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The structural stability of cereal grain materials such as wheat, triticale and corn were

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evaluated on the basis of DSC measurement. Fig. 1 shows the DSC thermograms of different

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cereals at different heating conditions. The results are summarized in Table 2. The different types

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of cereals show different endothermic transition peak and the enthalpy of fusion. The DSC curve

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of different cereals is characterized by a single predominant at 132.7, 133.5, & 131.5 °C for

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wheat, triticale and corn, respectively which is attributed to the thermal decomposition of cereals.

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Accordingly, the value of ∆Hf for wheat, triticale and corn was 122.1, 128.2 & 139.2 J/g,

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respectively. The thermal decomposition of cereals was not significantly different, whereas, heat 8 ACS Paragon Plus Environment

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enthalpy was remarkably different for three types of cereals. The SEM value was higher for ∆Hf

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in compare to that of Td.

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The thermal degradation behavior of cereals was significantly changed after moist and

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dry heat treatments. The position of the major endothermic peak of cereals at control, dry and

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moist autoclaved heating conditions was 131.7, 134.0 and 132.1°C, respectively. It is observed

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that endothermic peak position was shifted toward higher temperature from control (131.7 °C) to

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dry heating (134.0 °C), suggesting the high thermal stability of dry heated cereals. In contrast,

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the endothermic peak position was decreased to 132.1 °C in case of moist autoclaved heating

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which is comparable with non-heating control condition. These results are strongly support the

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published report regarding DSC analysis of wheat cooking (35). Stapley et al. (1997) (35) found

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that the DSC transition peak position depends on the moisture content of samples. In general,

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water content causes shifting endothermic peak at lower position. As autoclaved heating was

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done in the moist atmosphere the endothermic transition was shifted at lower temperature. It has

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been reported that the sensitivity of soybean seeds to moist heating was much higher than the

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sensitivity to dry heating in terms of the structure and nutrient profile changes (16). They showed

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that moist heating decreased protein rumen degradability and increased intestinal digestibility of

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rumen-undegradable protein. The increasing of intestinal digestibility of rumen-undegradable

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protein may be related to the thermal stability of the materials which is attributed by DSC

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endotherm profiles.

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Molecular Structural Changes by Raman Spectra

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The inherent protein structures influence the protein access to gastrointestinal digestive

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enzymes. Different α-helix-to-β-sheet ratio profiles affected the feed protein access to intestinal

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digestive enzymes (7). Even if tissues contain the same protein content, their nutritive value may 9 ACS Paragon Plus Environment


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be different if their α-helix to β-sheet ratios in their protein secondary structures is different. It

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has been reported that heat treatments changed the protein molecular structure of soybeans

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protein. Both dry and moist heating increased the amide I to amide II ratio. However, for the

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protein α-helix to β-sheet ratio, moist heating decreased but dry heating increased the ratio (16).

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To evaluate the influence of heat treatment on cereal feeds, we studied the percentage of protein

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secondary structure of wheat, triticale and corns after moist and dry heating. The results of

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deconvulated amide I bands of control, dry and autoclaved heat-treated cereals are shown in

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Table 3. There are marked differences in the secondary structures of the proteins subjected to

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various heating treatments of different cereals. Quantitative analysis revealed that control wheat

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had a composition corresponding to 39.0% α-helix, 38.8% β-sheet, 7.2% β-turns, 9.0% random

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coil and 5.5% aggregated strands. The ratio of α-helix and β-sheet was 1.01. After dry and moist

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heat treatments, wheat protein compositions were changed to 44.5% α-helix, 39.2% β-sheet,

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8.0% β-turns, and 8.7% aggregated strands for dry heat treatments and 29.2% α-helix, 50.7% β-

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sheet, 6.7% β-turns, 2.0% random coil and 11.3% aggregated strands for autoclaved moist heat

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treatments, respectively. The ratio of α-helix and β-sheet was 1.14 and 0.58 for dry and moist

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heat treatments, respectively. Accordingly, the secondary structures of triticale and corn also

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remarkably changed after dry and moist heat treatments. The alteration in the protein structure

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compositions and the ratio of α-helix and β-sheet was probably caused by denaturation of α-

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helix and β-sheet during the heating process. These results are consistent with the change of the

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protein structures of flaxseed tissues at different conditions reported by Doiron et al. (2009) (7).

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Multivariate Molecular Analysis

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Hierarchical cluster analysis (CLA) and principal component analysis (PCA) are two

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multivariate analyses that could be used for IR spectrum analysis (36). The multivariate analysis,

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CLA and PCA of Raman spectra for heat-treated cereal feeds were performed to discriminate

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and classify inherent structures between and among feed structures and feed molecular

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chemistry. Fig 2 shows the results of CLA and PCA of wheat, triticale and corns at different

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heating conditions for the ca. 2000–800 cm−1 spectral region based on Raman spectra. The

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cereals were in combined cluster at control condition and heat treatments did not show any

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significant individual cluster group (Fig 2a, 2b, 2c). PCA results revealed that three cereals were

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scattered although corn showed unique groups at control (Fig 2d) and autoclaved heating

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condition (Fig 2f), and triticale were grouped in almost separate ellipses after dry heating (Fig

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2e).

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The comparison by CLA and PCA of each cereal wheat, triticale and corn at different

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heating conditions for the ca. 2000–800 cm−1 spectral region based on Raman spectra were

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shown in Fig 3 and Fig 4, respectively. The cluster analysis comparisons show significantly

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isolated cluster group of each cereal at different heating conditions. Triticale was grouped

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separately from wheat in control (Fig 4a1) and after dry heating (Fig 4b1). Corn was shown

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perfectly separation from wheat in control samples (Fig 4a2), whereas, it was overlapped in both

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dry and autoclaved heating (Fig 4b2, 4c2). Triticale and corn spectral comparisons (Fig 4a3, 4b3,

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4c3) were not able to show any clear grouping of the spectra on the factor plane.

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To evaluate the effects of heating treatments in case of every individual cereal feeds,

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CLA and PCA analysis of control, dry and autoclaved heated cereals were carried out for the

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same Raman spectra region, ca. 2000–800 cm−1. The results show that the raw spectra were

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mixed up and did not isolate in their own spectra (Fig 5). For corn, the raw spectra for control

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and autoclaved specimen were shown individual group (Fig. 5). The comparison results between 11 ACS Paragon Plus Environment


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two treatments, dry and autoclaved heating, showed that autoclaved heating data were more

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isolated from control one in compare to dry heating condition (Fig 6 & Fig 7). Yu, (2005) [3]

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reported that Both PCA and CLA methods gave satisfactory analytical results and are conclusive

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in showing that they can discriminate and classify inherent structures and molecular chemistry

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between and among the feed tissues. They also can be used to identify whether differences exist

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between the varieties. Liu and Yu (13) applied CLA and PCA analysis to distinguish different

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genotypes of barley. Recently, Abeysekara et al. (37) revealed that characterization of protein

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molecular structure using non-invasive and non-destructive FTIR spectroscopy as a novel

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approach with univariate and multivariate molecular spectral analysis yielded positive results of

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proving the ability of DDGS to impose intrinsic molecular and structural chemical changes in

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other grains; barley, corn and oat. From the above CLA and PCA results, it might conclude that

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corn seed with autoclaved heating is more effective among three cereals: wheat, triticale and

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corn at control, dry and autoclaved heat treatments.

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CONCLUSION

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The different cereal grains, wheat, triticale and corn were heated at dry and moist heating

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conditions and their intrinsic molecular structural featured and thermal stability were evaluated

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in this study. The sensitivity of cereals to moist heating was much higher than the sensitivity to

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dry heating in terms of the structure and nutrient profile changes. The increasing of intestinal

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digestibility of rumen-undegradable protein may be related to the thermal stability of the

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materials which is attributed by DSC endotherm profiles.


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Raman spectroscopy results revealed that there are marked differences in the secondary

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structures of the proteins subjected to various heating treatments of different cereals. Both PCA

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and CLA methods gave satisfactory results and are conclusive in showing that they can

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discriminate and classify inherent structures between feed structures, which may be highly

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associated with feed quality and nutritive value and digestive behaviors in animals. Further

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investigation into the influence of molecular thermal stability and degradation affected by

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various heat processing conditions in relation to degradability and digestibility is required in feed

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materials.

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ACKNOWLEDGEMENTS

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We are grateful to Dr. Jason Maley, Saskatchewan Structural Science Centre (SCCC),

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University Of Saskatchewan for helping for Raman Spectroscopy measurement and Dr.

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Raghavan Jayaraman, Associate Professor and Director, Composite Materials and Structures

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Research Group, University of Manitoba for providing facility for Differential Scanning

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Calorimetry experiment, and The authors also thank Yuguang Ying and Zhi Yuan Niu,

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Department of Animal and Poultry Science, University of Saskatchewan for their support during

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the laboratory analysis. This work was financially supported by the ADF-Saskatchewan, Natural

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Sciences and Engineering Research Council of Canada (NSERC) and Ministry of Agriculture

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Strategic Research Chair Program (Saskatchewan, Canada)

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Conflict of Interest

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The authors declare no conflict of interest.



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20) Tuma, R. Raman spectroscopy of proteins: from peptides to large assemblies. Journal of Raman Spectroscopy 2005, 36, 307–319.

21) Privalov, P.L.

Stability of proteins: Small globular proteins. In Advances in protein

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chemistry; C.B. Anfinsen, J.T. Edsall, F.M. Richards, Eds. New York: Academic Press

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1979, 33, 167–241.

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22) Privalov, P.L. Stability of proteins: Proteins which do not present a single cooperative

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system. In Advances in protein chemistry; C.B. Anfinsen, J.T. Edsall, F.M. Richards, Eds.

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New York: Academic Press 1982, 35, 1–101.

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23) Remmele, R.L.; Nightlinger, N.S.; Srinivasan, S.; Gombotz, W.R. Interleukin-1 Receptor

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Pharmaceutical Research 1998, 15, 200–208.

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24) Wen, J.; Jiang, Y.; Narhi, L. Applications of DSC for Antibodies and Fc-Conjugated Proteins. American Pharmaceutical Review 2007, 10, 10–15. 16 ACS Paragon Plus Environment

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and canola seed oils. Journal of Food Composition and Analysis 2013, 30, 26–31.

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ruminal nutrient degradability of sunflower seed. Journal of the Science of Food and

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Agriculture 2003, 83, 1059–1064.

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major protein fraction from NorMan flaxseed (Linum usitatissimum L.). Food Chemistry

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2005, 90, 271–279.

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Popp, J. Nondestructive analysis of single rapeseeds by means of Raman spectroscopy.

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structure of unheated, heated, and high-pressure-treated beta-lactoglobulin and ovalbumin

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proteins using fourier transform raman spectroscopy and self-deconvolution. Journal of

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Agricultural and Food Chemistry 2004, 52, 6470–6477.

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structural analysis of two recombinant murine proteins, interleukins 1.alpha. and 1.beta.: Is

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infrared spectroscopy sufficient to assign structure? Biochemistry 1992, 31, 27−31.

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inherent molecular structures of barley, oat and corn combined with wheat DDGS.

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Page 19 of 30


Table 1. Tentative peak assignments of deconvoluted amide I components Fine structure Protein fine structure aggregated strands -sheet (low frequency) -sheet (high frequency) -turns -helix random coil

Amide I wavenumber (cm-1) ca. 1600-1620 ca. 1620-1640 ca. 1670-1680 ca. 1680-1699 ca. 1650-1660 ca. 1660-1670



Page 20 of 30

Table 2. DSC profiles of heat-induced cereals: comparison of raw (control) versus dry roasted and autoclaved wheat, triticale and corn: Effect of heat treatment Item Cereal

Wheat Triticale Corn SEM

Td (C) 132.7 133.5 131.5 0.60

Control Dry roasted Autoclaved SEM

131.7b 134.0a 132.1ab 0.60

Cereal (C) Heating method (H) C×H Interaction

0.065 0.026 0.823

Property ΔHf (J/g) 122.1b 128.2ab 139.2a 3.50

Heating method

Statistical analysis

141.0a 124.3b 124.2b 3.50 P value 0.004 0.001 0.948

Contrast Control vs. Heated 0.090 = 0.00

C T T

C

T

T

T

(c3)

C C

0 C -5

C T

-10

T C

T -15

-10

-10

-10

Projection of the cases on the factor-plane ( 1 x 2)

(b3)

-5

-20

-20

C

C

-15

-30

-30

15

T

T

W

C

5

C

C

Factor 2: 20.67%

C

C

C

-5

15

20

T

W C C

Factor 1: 62.04%

25

(a3)

W

0

Cases with sum of cosine square >= 0.00

C

(c2)

-15

30

5

W



C

Active

C

5

Factor 1: 72.29%

10

40

-20

Factor 1: 69.62%

15

30

W

-10

-10

-20 -50

0

W

W W

C

-15

-10

CC

C

-5

-20 -40

-20

W

5

-15

-30

20

10

10

C

10

W

15

15

w W

0

20

(b2)

C

10

0

-10


20

w

5

-20

25

Factor 2: 24.23%

w

-30


25

15

T

T

Factor 1: 71.74%


(a2)

T

W

-5

Factor 1: 68.66%

20

-20 -40

0

-20

Factor 1: 66.57%

25

W

W

-15

-20 -40

0

T

T W

T

-10

-20

-10

W 5

W

-15

-20

W W W

-15

-30

T

0

W

T

-10

T

Factor 2: 20.46%

Factor 2: 23.76%

5

T

W

10

W

5 w W

(c1)

15

10

10

-5

20

Factor 2: 19.59%

15

Factor 2: 22.31%


20

20

Factor 2: 12.34%



25

-40

-30

-20

-10

Factor 1: 70.62%

0

Factor 1: 70.93%

10

20

30

40

-20 -40

Active

-30

-20

-10

0

10

20

30

40

Active

Factor 1: 67.95%

Figure 4. Principal components analysis of Raman spectra (ca.2000–800 cm−1) obtained from: (a) control (without heating); (b) dry roasted (at 121C for 80 min); and (c) autoclaved (moist heated at 121C for 80 min) cereals; Comparisons of different cereals at different treatments (1: W vs. T, 2: W vs. C, 3: C vs. T); (W = wheat, T = triticale, C = corn). ACS Paragon Plus Environment

4


Page 26 of 30

Tree Diagram for 18 Cases Projection of the cases on the factor-plane ( 1 x 2)

Ward`s method


Euclidean distances 20

1.2E5

15 1E5

0

10

0

2

1

1 5

Factor 2: 21.38%

Linkage Distance

80000

60000

40000

1

00

0 2 1 1

0

2 2

-5

2

-10

0

2 1

-15 20000 -20 -25 -40

0 1

0

2

1

2

1

1

0

0

2

0

0

2

2

2

1

1

0

-30

-20

-10

0

10

20

30

40

Active

Factor 1: 68.86%

(a)

(d)

Tree Diagram for 18 Cases Projection of the cases on the factor-plane ( 1 x 2)

Ward`s method


Euclidean distances

20

2E5

15 1

10 1.5E5

1

Factor 2: 19.84%

Linkage Distance

5

1E5

50000

2

2

1 0

0

1

0

2 2

0

1

1

0

0 -5 0 -10

2 2

-15 -20 -25 -35

0 1

2

1

0

1

2

2

0

1

2

2

2

1

0

0

1

0

0

-30

-25

-20

-15

-10

-5

0

5

10

15

20

25

30

Active

Factor 1: 67.64%

(b)

(e)

Tree Diagram for 18 Cases


Ward`s method


Euclidean distances

35

1.4E5

30 25

1.2E5

1

20

Factor 2: 25.80%

Linkage Distance

1

15

1E5

80000

60000

10 5

1

0

0

0 0

1 1 20

0

0

1

-5 2

22

2

-10 2

40000

-15 -20

20000

-25

0 1

2

2

2

0

1

1

0

1

1

0

0

2

2

0

1

2

0

-30 -60

-50

-40

-30

-20

-10

0

10

20

30

Active

Factor 1: 65.69%

(c)

(f) −1

Figure 5. Multivariate analysis of Raman spectra (ca. 2000–800 cm ) for different cereals: (a, d) wheat; (b, e) triticale and (c, f) corn. [0 – control (without heating); 1 - dry roasted (at 121C for 80 min); 2 - autoclaved (moist heated at 121C for 80 min)]; (a, b, c): Hierarchical cluster analysis; (d, e, f): Principal components analysis.

ACS Paragon Plus 5 Environment

Page 27 of 30

Journal of Agricultural and Food Chemistry Tree Diagram for 12 Cases


Ward`s method

Ward`s method

Euclidean distances

90000

Euclidean distances

1.4E5

(a1)

80000

.


Ward`s method

Euclidean distances

1.2E5

(b1)

1.2E5

1E5

60000 50000 40000 30000

Linkage Distance

80000

Linkage Distance

Linkage Distance

(c1)

1E5

70000

80000

60000

60000

40000

40000 20000 20000

20000

10000 0 1

0

1

1

1

0

1

0

0

0

1

0

0

0 1

1

0

1

0

1

1

0



Ward`s method

Ward`s method

Euclidean distances

Euclidean distances

90000

0

1

0

0

1

0

1

1

0

1

0

0

1

0

0

Tree Diagram for 12 Cases Ward`s method Euclidean distances 80000

1.4E5

(a2)

80000

1

(b2)

1.2E5

(c2)

70000

70000

60000 1E5

50000 40000 30000

Linkage Distance

Linkage Distance

Linkage Distance

60000 80000

60000

40000

50000 40000 30000 20000

20000 20000

10000 0

10000 0

0

0

2

2

0

0

2

0

0

2

2

2

0

2

2

2

0


0

2

2

0

0

2

0

2

0

2

0

2


Ward`s method

0

Ward`s method

Euclidean distances

(a3)

2

2

0

2

0

Euclidean distances

1.6E5

1E5

0

Ward`s method

Euclidean distances

1.2E5

0


1.2E5

(b3)

1.4E5

(c3)

1E5

1.2E5

60000

40000

Linkage Distance

80000

Linkage Distance

Linkage Distance

80000 1E5 80000 60000

60000

40000

40000 20000

20000 20000

0

0 1

2

2

1

2

1

1

2

2

1

2

1

0 1

2

2

1

2

1

2

2

1

2

1

−1

1

1

1

1

1

2

2

2

1

2

2

2

1

Figure 6. Hierarchical cluster analysis of Raman spectra (ca. 2000–800 cm ) for different cereals: (a) wheat; (b) triticale and (c) corn; Comparisons of different cereals at different treatments (1: 0 vs. 1, 2: 0 vs. 2, 3: 1 vs. 2); [0 – control (without heating); 1 - dry roasted (at 121C for 80 min); 2 - autoclaved (moist heated at 121C for 80 min)].


Journal of Agricultural and Food Chemistry Projection of the cases on the factor-plane ( 1 x 2)


0 00

-5 -10

0

-15

15

10

1 1

0

1

5

0

1 0

1

0

1

1

0

0 -5

0

1

-10

-20

-10

0

10

20

30

40

-30


-20

-10

0

10

20

30

40

-20 -50

Factor 2: 19.51%

2 2 0

-10

0

10

20

0

0 0

2

0

(b2)

Active

-5

(c2)

10

0 20

0

2

0

0

0

5

0

0

0

22

0 -5

2 2

-10 -15

2

-10

2

30


2 0 2

2

-10

15

5

-5

-20


10 2

0

-30

20

2 00

10

Factor 1: 70.18%


(a2)

5

-40

Active

15

0

1 0 0



15

0

0 0

Factor 1: 69.06%

20

0

1

1

0

-15

-20 -40 Active

Factor 1: 73.47%

10

5

-10

-15

-30

1 10

-5

0

-20

(c1)

1

20

Factor 2: 24.47%

Factor 2: 19.60%

1

0

25

1

15

Factor 2: 16.00%

5

(b1)

20

1

1

30

Factor 2: 23.07%

(a1) 0

Factor 2: 20.85%


25

10

-25 -40



Cases with sum of cosine square >= 0.00 15

Page 28 of 30

2

2 -20

-15

-15 -25 -30

-20

-10

0

10

20

30

40

50

-20 Active-35

-30

-25

-20

-15

-10

Factor 1: 70.95%

-5

0


10

15

20

25

30

-30 -40

Active

20

(a3) 1

1

Factor 2: 19.31%

1 2 1 1

0 2

2

2

-10

-10

-5

0

5

10

25

15

20

25

Active

1

1

2

2

2

15

2

0

1

1 -5 -10

1

10 5

1 1 2

1 0

1

22

-5

2

-10

2

2

2

1

(c3)

1

20

1

5

5

-15


(b3)

10

2

-20


15

1

-5

-25

30

10

2

-30


15

-35

Factor 1: 60.43%



Factor 2: 22.48%

5

Factor 1: 70.53%

Factor 2: 27.00%

-20 -40

2

-15

-15

-15 -20 -25 -40

-20

-30

-20

-10

0

10

Factor 1: 66.84%

20

30

40

-25 Active -40

-20

-30

-20

-10

0

10

20

30

40

-25 -50

Active

Factor 1: 70.07%

-40

-30

-20

-10

0

10

20

30

Active

Factor 1: 66.18%

Figure 7. Principal components analysis of Raman spectra (ca. 2000–800 cm−1) for different cereals: (a) wheat; (b) triticale, and (c) corn; Comparisons of different cereals at different treatments (1: 0 vs. 1, 2: 0 vs. 2, 3: 1 vs. 2); [0 – control (without heating); 1 - dry roasted (at 121C for 80 min); 2 - autoclaved (moist heated at 121C for 80 min)].


Page 29 of 30




Page 30 of 30

Table of contents (TOC) graphic

Molecular Thermal Stability and Molecular Mobility of HeatInduced Cereal Grains

20

40

60

80 100 120 140 160 180 200

0.0

Wheat

-2.0

Endo

Exo

-4.0

-6.0 -8.0 -10.0

Control

-12.0

Autclaved heated

-14.0

-16.0

Dry heated

Temperature ( C)

ACS Paragon Plus Environment

Thermal Stability and Molecular Microstructure of Heat-Induced Cereal

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