**E**ff**ects of Photoperiod Regime on Meat Quality, Oxidative Stability, and Metabolites of Postmortem Broiler Fillet (***M. Pectoralis major***) Muscles**

**Jacob R. Tuell 1 , Jun-Young Park 1,2 , Weichao Wang 3 , Bruce Cooper 4 , Tiago Sobreira 4 , Heng-Wei Cheng 5 and Yuan H. Brad Kim 1,\***

Received: 3 February 2020; Accepted: 14 February 2020; Published: 19 February 2020

**Abstract:** The objective of this study was to evaluate the effects of photoperiod on meat quality, oxidative stability, and metabolites of broiler fillet (*M. Pectoralis major*) muscles. A total of 432 broilers was split among 4 photoperiod treatments [hours light(L):dark(D)]: 20L:4D, 18L:6D, 16L:8D, and 12L:12D. At 42 days, a total of 48 broilers (12 broilers/treatment) was randomly selected and harvested. At 1 day postmortem, fillet muscles were dissected and displayed for 7 days. No considerable impacts of photoperiods on general carcass and meat quality attributes, such as carcass weight, yield, pH, water-holding capacity, and shear force, were found (*p* > 0.05). However, color and oxidative stability were influenced by the photoperiod, where muscles from 20L:4D appeared lighter and more discolored, coupled with higher lipid oxidation (*p* < 0.05) and protein denaturation (*p* = 0.058) compared to 12L:12D. The UPLC–MS metabolomics identified that 20 metabolites were different between the 20L:4D and 12L:12D groups, and 15 were tentatively identified. In general, lower aromatic amino acids/dipeptides, and higher oxidized glutathione and guanine/methylated guanosine were observed in 20L:4D. These results suggest that a photoperiod would result in no considerable impact on initial meat quality, but extended photoperiods might negatively impact oxidative stability through an alteration of the muscle metabolites.

**Keywords:** antioxidative status; aromatic amino acids; broiler; lighting program; meat quality; metabolite profiling

#### **1. Introduction**

Broiler chicken production plays a key role in supplying consumers with high quality protein, as evidenced by the steady increase in US broiler chicken production from 50.4 billion pounds in 2008 [1] to 56.8 billion pounds in 2018 [2]. While genetic improvement plays the most critical factor in meeting this continuously rising demand [3,4], environmental factors such as rearing temperature, nutrition, and lighting schedule/intensity must be managed to fully capitalize on the genetic potential of modern broiler chickens [5–7].

The poultry industry has traditionally reared broilers under long photoperiods to maximize growth performance [7,8]. Several studies have identified continuous or near-continuous photoperiod regimes as positively impacting breast meat yield, feed consumption and conversion, and growth rates [9–11]. However, there is growing evidence that long photoperiods might negatively impact broiler health and welfare. For example, rapid growth rates in broiler chickens have been implicated in causing skeletal deformities, metabolic disorders, and increased mortality [7,12]. The increased breast meat yield commonly observed in long photoperiod regimes has often been shown to be inversely related to the yield of the thigh and drum [10,11,13], contributing to an increased frequency of leg abnormalities with impaired walking ability [9,14,15]. These detriments to skeletal health associated with long photoperiods might also associate with the disruption of the normal diurnal rhythm, which plays a critical role in regulating bone modeling/remodeling [16].

Several studies have investigated the impacts of photoperiod on broiler performance and carcass traits, but few studies have examined its potential impacts on meat quality. Previously, Li et al. [17] found that breast meat from broilers reared under a 12L:12D photoperiod has lower malondialdehyde (MDA, a secondary lipid oxidation product) concentrations compared to 23L:1D controls. Guob et al. [18] corroborated this finding, reporting higher blood serum MDA levels in broilers reared at a longer photoperiod, indicative of increased oxidative stress. It has been well-established that chronic stress is detrimental to broiler meat quality and oxidative stability [19–21]. However, to our knowledge, no studies have evaluated the impact of photoperiod on meat quality and oxidative stability of broiler fillet (*M. Pectoralis major*) muscles during aerobic display. Aerobic packaging (using oxygen permeable polyvinylchloride film/overwrap with a polystyrene foam tray) is the most common method of packaging for fresh broiler meat products in the US [22], despite often exhibiting a shorter shelf-life [23,24]. As such, aerobic display storage could potentially exacerbate any oxidative defect already present in fresh broiler products. The aim of this study was to evaluate the effects of photoperiod on meat quality, oxidative stability, and metabolites of postmortem broiler fillet (*M. Pectoralis major*) muscles.

#### **2. Materials and Methods**

All animal use and procedures were approved by the Purdue Animal Care and Use Committee<PHONE_NUMBER>).

#### *2.1. Photoperiod Treatments*

Ross 308 broiler chicks (*n* = 432) at 1 day of age were weighed in groups (*n* = 18/group) and allocated among 24 pens (110 cm × 110 cm) for equal distribution of weight across the pens. The pens were randomly assigned to one of four photoperiod treatment rooms at the Poultry Unit of the Animal Sciences Research and Education Center at Purdue University. Lighting schedule regimens were performed as follows: [hours light(L):dark(D)] 20L:4D, 18L:6D, 16L:8D, and 12L:12D. For all treatments, the birds were provided with constant (24L:0D) lighting at 30 lux at 1 day of age, reduced to 23L:1D from day 2 to day 7. After this, the photoperiods were adjusted in gradual increments until reaching the final expected photo schedule at day 14, which were maintained until 42 days of age. Brooder temperature was 34 ◦C until day 3, after which temperature was gradually reduced until 21–24 ◦C was reached and maintained until 42 days of age.

All broilers were provided a starter diet with 23.43% crude protein (CP) and 3,050 kcal metabolizable energy (ME)/kg from day 1 to day 14, a grower diet with 22.81% CP and 3,150 kcal ME/kg from day 15 to day 28, and a finisher diet with 19.17% CP and 3,200 kcal ME/kg from day 29 until day 42. Food and water was provided ad libitum throughout the course of the study.

#### *2.2. Harvest and Sample Preparation*

At day 42, 2 broilers per pen (*n* = 12/treatment; a total of 48 broilers) were randomly selected, transported approximately 30 min, and harvested under the standard procedures. The hot carcass weight (HCW) was recorded as the weight of the carcass following plucking, evisceration, and removal of the head and feet, prior to carcass chilling. The carcasses were chilled in a commercial air cooler

with an ambient temperature of 2 ◦C. The chilled carcass weight (CCW) was recorded as the weight of the carcass after 24 h of chilling. Cooler shrink was calculated as the percent difference between the HCW and CCW. Broiler fillet (*M. Pectoralis major*) muscles, with *M. Pectoralis minor* and skin removed, were dissected from both sides of each carcass at 1 day postmortem and weighed. Fillet yield was calculated as the weight of the fillet muscles as a percentage of CCW.

Approximately 30 g of each left side fillet muscle was collected for drip loss measurement. The remaining left side muscles were frozen and stored at −80 ◦C as 1 day postmortem samples until later analyses. The right side fillets were displayed for 7 days at 2 ◦C under 1450 lux fluorescent lighting to mimic the retail store conditions. The samples were displayed bone-side up on polystyrene foam trays with soaking pads, overwrapped with a commercial polyvinyl chloride film. After aerobic display storage, the samples were frozen and stored at −80 ◦C as 7 day displayed samples. Prior to analysis, the samples were frozen in liquid nitrogen and pulverized to form a homogenous powder by using a commercial blender (Waring Products, Inc., Stamford, CT, USA).

#### *2.3. pH Measurement*

The pH of the fillet muscles was measured at 1 d postmortem, following carcass chilling. Values were obtained from the left fillet muscles in duplicates by using a pH probe (Hanna Instrument, Inc., Warner, NH, USA) calibrated with pH 4 and 7 buffers prior to the analysis.

#### *2.4. Water-Holding Capacity (WHC)*

Prior to any measure of WHC, the samples were gently blotted with a paper towel to remove excess moisture from the muscle surface. Drip loss was measured in accordance with the method published by Honikel [25], with some modifications. At 1 day postmortem, approximately 30 grams of the caudal portion of the left side fillet muscles without skin and visible connective tissue was suspended with netting in an airtight container, at 2 ◦C for 48 h. The drip loss was expressed as the percentage difference between the weight of the sample prior to and after hanging storage. Display weight loss was determined as the percent difference between the initial and final weights of the samples before and after 7 days of aerobic display storage. Freezing/thawing loss was assessed as the percent difference between weights prior to and after freezing/thawing of 1 day postmortem samples at –80 ◦C and 24 h of thawing at 2 ◦C. Cooking loss was measured by cooking samples in a water-impermeable plastic bag submerged in an 80 ◦C water bath. Cooking temperature was monitored using a thermocouple (Type-T, Omega Engineering, Stamford, CT, USA) connected to a data logger (Madge Tech, Inc., Warner, NH, USA). After 71 ◦C was reached in the geometrical center of the fillet, the samples were immediately submerged in an ice water bath to halt the cooking process. Cooking loss was expressed as the percent change between the initial and final weight of the samples.

#### *2.5. Instrumental Tenderness*

The samples used for cooking loss were chilled at 4 ◦C for 16 h prior to the measurement of shear force. Six slices (1 cm × 1 cm) per sample were taken in a direction parallel to that of the muscle fibers. Slices were sheared perpendicularly to the fiber direction using a Warner-Bratzler type V-shaped blade attached to a TA-XT Plus Texture Analyser (Stable Micro System Ltd., UK) at 2 mm/sec. Peak shear force from the cores in Newtons was determined, and the mean values of the replicates were used for statistical analysis.

#### *2.6. Proximate Composition*

Proximate composition of the fillet muscles at 1 day postmortem was analyzed according to the AOAC methods [26]. Moisture was determined in triplicates at 100 ◦C using the oven air-drying method. Percent nitrogen was measured in duplicates following the Dumas combustion method (Leco, St. Joseph, MI, USA), and concentration of crude protein was determined by multiplying percent nitrogen by 6.25. Crude ash was measured in duplicates by combusting the dried samples in a 580 ◦C muffle furnace. Crude lipid was determined as follows, by the formula:

100% − [% moisture + % crude protein (wet matter basis) + % crude ash (wet matter basis)] (1)

#### *2.7. Instrumental Color Attributes*

Instrumental color was assessed daily on 7 day displayed samples. Commission internationale de l'éclairage (CIE) L\*, a\*, and b\* values were obtained in triplicates from three randomly selected locations per fillet using a CR-400 Chroma Meter (Konica Minolta, Chiyoda, Tokyo, Japan) equipped with a CIE standard illuminant D65. CIE a\* and b\* data were used to determine the hue angle (discoloration) and chroma (saturation) values based on the American Meat Science Association meat color measurement guidelines [27].

#### *2.8. Oxidative Stability and Transmission Value*

Lipid oxidation was assessed using the 2-thiobarbituric acid reactive substances (TBARS) assay to determine the formation of MDA according to the method published by Buege and Aust [28] with modifications for broiler meat by Kim et al. [29]. Values of both day 1 and day 7 samples were obtained in duplicates by measuring absorbance of the obtained supernatant at 531 nm using a microplate spectrophotometer (Epoch, Biotek Instruments, Inc., Winooski, VT, USA) and multiplying by 5.54 to calculate the TBARS value. The values were expressed as milligrams MDA per kilogram of fillet muscle.

Protein oxidation, by measuring loss of thiol groups on day 1 and day 7 of aerobic display storage, was assessed in duplicates following the method published by Berardo et al. [30]. Absorbance of the sample filtrate was measured at 412 nm using a microplate spectrophotometer (Epoch, Biotek Instruments, Inc., Winooski, VT, USA), and the values of the thiol content presented in nanomoles thiol groups per milligram protein were determined using the Lambert–Beer formula (ε412 = 14,000 M−1 cm−1 ). Protein concentration of the sample filtrate was assessed using a bovine serum albumin standard curve.

Transmission values, a determinant of protein denaturation, of day 1 postmortem samples were assessed in duplicates using the method of Ockerman and Cahill [31] with modifications as described by Kim et al. [32]. Briefly, turbidity of the mixed sample, 1 milliliter of sample filtrate mixed with 5 milliliters of 0.1 M citric acid in 0.2 M sodium phosphate buffer (pH 4.6), was measured at 600 nm wavelength using a spectrophotometer (UV-1600PC, VWR International, LLC, Radnor, PA, USA).

#### *2.9. Fatty Acid Profile*

Intramuscular lipids were extracted from the fillet muscles in duplicates using the method of Folch et al. [33] with modifications by Shin and Ajuwon [34]. Briefly, fatty acid methyl esters (FAME) were prepared by a trans-esterification reaction, after which FAMEs were extracted in hexane. FAMEs were analyzed on a gas chromatograph (Varian CP 3900 with CP-8400 autosampler, Agilent, Santa Clara, CA, USA) equipped with a 105 m Rtx-2330 fused silica capillary GC column (10729, Restek, Bellefonte, PA, USA). Fatty acids were identified by comparison of retention times with the known standards (Supelco 37 components FAME Mix, Sigma Aldrich, St. Louis, MO, USA). Detected fatty acids were expressed in grams per 100 grams of intramuscular lipid.

#### *2.10. UPLC–MS Metabolite Profiling*

For metabolomics data, one sample per pen of the two extreme treatments (20L:4D and 12L:12D) was randomly selected and analyzed. Sample extraction and removal of protein was performed in accordance to the method published by Bligh and Dyer [35]. In brief, 100 mg of fillet muscle tissue powder was vortexed with 300 µL of chloroform and 300 µL of methanol, after which 300 µL of distilled water was added and centrifuged for 10 min at 16,000× *g*. The upper phase, containing the polar metabolites, was transferred to a microcentrifuge tube, evaporated to dryness with a vacuum concentrator, and reconstituted in 60 µL of HPLC diluent (95% water and 5% acetonitrile, containing 0.1% formic acid). Samples were sonicated, centrifuged, and transferred to HPLC vials.

UPLC–MS was performed using an Agilent 1290 Infinity II UPLC system (Agilent Technologies, Palo Alto, CA, USA) equipped with Waters Acquity HSS T3 separation column (2.1 mm × 100 mm, 1.8 µm) and HSS T3 guard column (2.1 mm × 5 mm, 1.8 µm) (Waters, Milford, MA, USA). A gradient of water and acetonitrile was used. Following chromatographic separation, an Agilent 6545 quadrupole time-of-flight (Q-TOF) mass spectrometer was employed. Mass data were collected and analyzed with Agilent MassHunter B.06 software from an m/z of 70–1,000. Agilent Reference Mass Correction Solution (G1969-85001) was infused to improve mass accuracy. Agilent ProFinder B.10 was employed for peak deconvolution and alignment. Peaks were annotated using the HMDB (www.hmdb.ca) metabolite database with a mass error ≤ 10 ppm.

#### *2.11. Statistical Analysis*

The experimental design was a randomized complete block with the photoperiod treatment (20L:4D, 18L:6D, 16L:8D, and 12L:12D) as the fixed effect and the pen (*n* = 6/treatment) as the experimental unit. Individual broilers and their interactions with the fixed effect were considered as a random effect. Data including the aerobic display storage period (i.e., color and oxidative stability) were considered as a split plot design, where the photoperiod treatment was a whole plot and the display duration was a sub-plot. Data were analyzed using the PROC MIXED and PROC GLIMMIX procedures of SAS 9.4 (SAS Institute Inc., Cary, NC, USA) with the PDIFF option for separation of the least square means (*p* < 0.05). Trends were defined as (0.10 > *p* ≥ 0.05). For metabolomics data, one sample from each photoperiod treatment lacked adequate correlation with others within the respective treatment and was omitted from the analysis, resulting in 5 replicates per treatment. Metabolomics data were analyzed using the unpaired *t*-test significance analysis, and significant metabolites (*p* < 0.05) were used for principal component analysis (PCA) modeling.

#### **3. Results**

#### *3.1. Carcass and Meat Quality*

Effects of photoperiod on carcass traits including HCW, CCW, cooler shrink, fillet weight, and fillet yield are presented in Table 1. Overall, the photoperiod did not result in any considerable impacts on general carcass quality attributes (*p* > 0.05). However, there was a tendency for carcasses from 20L:4D to lose more weight, as shown by the greater cooler shrink percentage compared to other shorter photoperiod treatments (*p* = 0.070).

**Table 1.** Effect of photoperiod on broiler carcass characteristics (*n* = 6/treatment).

Similarly, no significant effects of photoperiod on pH, WHC, and instrumental tenderness of broiler fillet muscles were found (Table 2, *p* > 0.05). There was a trend of fillets from 16L:8D showing higher freezing/thawing loss compared to others (*p* = 0.098).

Proximate composition of the fillet muscles was unaffected by the treatments as well (Table 3, *p* > 0.05).

**Table 2.** Effect of photoperiod on pH, water-holding capacity, and instrumental shear force of broiler fillet (*M. Pectoralis major*) muscles (*n* = 6/treatment).

**Table 3.** Effect of photoperiod on proximate composition (wet-matter basis) of broiler fillet (*M. Pectoralis major*) muscles (*n* = 6/treatment).

#### *3.2. Color, Oxidative Stability, and Fatty Acid Profile*

There were significant interactions between the photoperiod treatment and aerobic display storage on CIE L\* (lightness), CIE a\* (redness), CIE b\* (yellowness), and hue angle (discoloration) values of the fillet muscles (Table 4, *p* < 0.05). At day 1 of display, the muscles from 20L:4D exhibited a lighter color than the muscles from 18L:6D and 12L:12D (*p* < 0.05) but not 16L:8D (*p* > 0.05). At day 2 of display and forward, 18L:6D fillets had lower CIE L\* values compared to the 20L:4D only (*p* < 0.05), while 16L:8D and 12L:12D were intermediates (*p* > 0.05). For CIE a\* values, the fillets from 16L:8D were higher than other treatments on day 1 of display (*p* < 0.05), although both 18L:6D and 12L:12D were redder in color than 20L:4D (*p* < 0.05). By day 7 of display, 16L:8D fillets maintained higher redness than 20L:4D (*p* < 0.05), but were not different from 18L:6D and 12L:12D groups (*p* > 0.05). For CIE b\*, there were no differences across treatments from day 1 to day 5 of display (*p* > 0.05). However, on day 6 and day 7, the fillets from 18L:6D were less yellow in color than 16L:8D (*p* < 0.05) only.

A significant interaction between the photoperiod treatment and the display time on hue angle values was found (Table 4). The highest hue angle values (indication of discoloration) were observed in 20L:4D compared to other photoperiod treatments on day 1 and day 2 of display (*p* < 0.05). This could be attributed to the lower CIE a\* values observed in 20L:4D during the same display duration, coupled with numerically higher CIE b\* values. From day 3 and onwards, the differences across treatments on each respective display day were less pronounced, although 18L:6D maintained a lower hue angle than 20L:4D (*p* < 0.05) but was not different compared to 16L:8D and 12L:12D treatments at any point of the display (*p* > 0.05). Chroma values were affected by display storage duration only (*p* < 0.05, data not shown), regardless of the photoperiod group (*p* > 0.05). Overall, chroma values exhibited a similar pattern as CIE b\* values, increasing from day 1 to day 4 of display before decreasing by day 7.

Significant main effects of the photoperiod and the display period were observed for the TBARS values (Table 5, *p* < 0.05). Higher concentration of MDA was found in the fillet muscle samples from 20L:4D (0.50 mg MDA/kg fillet) and 18L:6D (0.52 mg MDA/kg fillet), compared to the 12L:12D (0.37 mg MDA/kg fillet) (*p* < 0.05), while 16L:8D (0.42 mg MDA/kg fillet) was intermediate (*p* > 0.05). As expected, MDA accumulated in the displayed samples from day 1 to day 7 (*p* < 0.05). There was no significant interaction between the photoperiod and the display observed in the TBARS values. There was, however, an interaction between the photoperiod and the display period for protein oxidation, as assessed by the content of the thiol groups (*p* < 0.05). A detectable loss in thiol groups was found in 20L:4D from day 1 to day 7 only (*p* < 0.05).

**Table 4.** Effect of photoperiod on D65 instrumental color attributes [CIE L\* (lightness), CIE a\* (redness), CIE b\* (yellowness), hue angle (discoloration), and chroma (color intensity)] of broiler fillet (*M. Pectoralis major*) muscles during 7 days of aerobic display (*n*=6/treatment).

1 Photoperiod effect. 2 Display period effect. a–k Means lacking a common superscript within a color attributediffer due to the interaction of photoperiod treatment and display period (*p*<0.05).

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**Table 5.** Effect of photoperiod on the 2-thiobarbituric acid reactive substance values, thiol content, and transmission value of broiler fillet (*M. Pectoralis major*) muscles, during aerobic display storage (*n*=6/treatment).

1 Photoperiod effect. 2 Display period effect. 3 2-thiobarbituric acid reactive substances values expressed as milligrams MDA per kilogram fillet muscle. 4 Thiol content expressed as nanomoles thiol groups per milligrams protein. a,b Means lacking a common superscript within a row differ due to photoperiod treatment (*p* < 0.05). x,y Means lacking a common superscript within a row differ due to display period (*p* < 0.05). A,B Means lacking a common superscript differ due to the interaction of the photoperiod treatment and the display period (*p* <0.05).

In terms of protein denaturation, there was a strong trend that the fillet muscle samples from 20L:4D and 16L:8D had a higher transmission than the samples from 18L:6D and 12L:12D groups (*p* = 0.058), indicating a higher degree of denaturation in the sarcoplasmic protein in those samples.

Most detected fatty acids were not different across the photoperiod treatments (Table 6, *p* > 0.05). Higher polyunsaturated fatty acids (PUFA) were found in lower photoperiod groups (16L:8D and 12L:12D) compared to 20L:4D (*p* < 0.05). Of the PUFA, higher omega-3 fatty acids were found in 12L:12D compared to the 20L:4D (*p* < 0.05), while higher omega-6 fatty acids were found in both 16L:8D and 12L:12D compared to 20L:4D (*p* < 0.05). The differences in omega-3 and omega-6 fatty acid contents were not pronounced enough to cause a significant difference in the omega-6:omega-3 ratio (*p* = 0.114), nor the ratio of unsaturated to saturated fatty acids (*p* = 0.588). There was, however, a tendency for higher palmitic (C16:0) acid in 20L:4D compared to other photoperiod groups (*p* = 0.082).

**Table 6.** Effect of photoperiod on the fatty acid profile (grams per 100 grams of intramuscular lipid) of broiler fillet (*M. Pectoralis major*) muscles (*n* = 6/treatment).

1 Saturated fatty acids. 2 Monounsaturated fatty acids. 3 Polyunsaturated fatty acids. 4 Unsaturated fatty acids. a,b Means lacking a common superscript within a row differ due to the photoperiod treatment (*p* < 0.05).

#### *3.3. Metabolite Profiling*

Metabolite profiling was conducted for the samples from the two extreme treatments (20L:4D and 12L:12D) in order to obtain insight into the biological and biochemical processes that might be differently affected by the photoperiod treatments. Untargeted metabolite profiling detected 1472 metabolites in the fillet muscle samples from the 20L:4D and 12L:12D treatments. PCA was performed to discriminate between the 20L:4D and 12L:12D treatments based on the 20 significant metabolites with *p* < 0.05 (Figure 1). PC1 was shown to explain 31.65% of the total variance and PC2 as 17.02%, leading to a total PC of 48.67%.

Of the 20 metabolites found to be significantly impacted by the photoperiod treatment, 15 were tentatively identified using the HMDB metabolite database with a mass error of ≤ 10 ppm (Table 7). Overall, the muscles from 12L:12D were found to be higher in amino acids/dipeptides of aromatic amino acids (tyrosine, tryptophan, phenylalanine) with leucine/isoleucine, as well as piperidine. Samples from 20L:4D were found to have a higher oxidized glutathione, methylated histidine, and guanine and methylated/demethylated guanosine (*p* < 0.05).

**Figure 1.** PCA modeling based on the 20 metabolites present within the broiler fillet (*M. Pectoralis major*) muscles were found to be different (*p* < 0.05) between the 20L:4D and 12L:12D photoperiod treatment groups (*n* = 5/treatment).

**Table 7.** Metabolites significantly different between the 20L:4D and 12L:12D photoperiod treatments (*n* = 5/treatment).

1 Compounds were tentatively identified using the HMDB (www.hmdb.ca) metabolite databases with a mass error ≤ 10 ppm. 2 Fold change from 20L:4D to 12L:12D. Positive fold change indicates higher levels in the 20L:4D group and lower levels in the 12L:12D group. Negative fold change indicates lower levels in the 20L:4D group and higher levels in the 12L:12D group.

#### **4. Discussion**

Previous studies evaluating the influence of photoperiod on broiler meat production have primarily focused on growth performance and yield [7,8]. Several studies have reported a positive impact of the length of photoperiods on growth performance during the starter phase and breast meat yield [10,11,13]. The present study observed no impact of photoperiod on HCW and CCW, agreeing with previous literatures which showed that compensatory gain occurring in the finishing stage minimizes the differences in market weight [11,13,17]. In addition, the present study found no impact of the photoperiod on the weight and yield of the fillet muscles, in disagreement to previous findings [10,11,13,17], except that the percentage of cooler shrink was linearly receded with a shorter photoperiod. In particular, there was a tendency (*p* = 0.070) for 20L:4D carcasses to lose more moisture during the carcass chilling process compared to the 16L:8D and 12L:12D groups. The difference in the current study might be caused by multiple factors, such as the rearing conditions (pen size, group density, and room temperature) or combination of test factors (light intensity, feed energy, and nutrient density). These findings do indicate, however, that the photoperiod-associated differences in meat quality cannot be attributed to weight differences of the fillet muscles.

In addition, aside from a trend of higher freezing/thawing loss in 16L:8D (*p* = 0.098), there was no other impact of photoperiod on WHC. Similarly, other studies have found no impact of photoperiod on WHC [17,36]. No differences in shear force values were also observed across treatments, and, to our knowledge, studies measuring instrumental tenderness of broiler fillet muscles associated with photoperiod effects are unavailable in the current literature. Li et al. [17] reported a higher percentage meat protein in broiler fillet muscles from 12L:12D compared to 20L:4D and 23L:1D groups. Proximate composition including crude protein concentration was unaffected in the present study. This could be explained in part by the lack of a 23L:1D group in the present study, as the meat protein values reported by Li et al. [17] were 0.67% different between the 23L:1D and 12L:12D groups but only 0.32% different between the 12L:12D and 20L:4D groups, The current and previous findings support the postulation that the photoperiod regimes of the present study are unlikely to have any considerable impacts on the composition and general meat quality attributes of the broiler fillet muscles.

In terms of oxidative stability during chilling storage/display times, photoperiod treatments had multiple main effects and two-way interactions on instrumental color and oxidative stability attributes. The fillet muscles from the 20L:4D and 18L:6D groups had higher MDA contents than the 12L:12D group. This finding is interesting considering the lower PUFA content in 20L:4D compared to 12L:12D, as well as 16L:8D, as it has been well-established that PUFA is much more susceptible to lipid oxidation [37]. However, it has been demonstrated that broiler thigh muscle is less susceptible to lipid oxidation during refrigerated storage than breast meat, despite higher free long-chain PUFA content [38]. Numerous factors can promote lipid oxidation in fresh broiler muscles including microbial growth, enzymatic activity, exposure to light and oxygen, and others [39]. As all broilers were treated in the same manner during and following harvest, it is likely differences in oxidative stability can be attributed to pre-harvest factors. The samples from 20L:4D showed a significant decrease in thiol contents from day 1 to day 7, while the samples from other groups had no difference in the thiol contents during display. The finding of longer photoperiod being pro-oxidative to broiler muscles has been corroborated by Li et al. [17] who observed a higher MDA in the fillet muscles from 23L:1D compared to 12L:12D. In addition, Guob et al. [18] reported that a 12L:12D treatment decreased the serum MDA content compared to 20L:4D. The findings were further supported by a reported trend of greater activity of superoxide dismutase, a well-known indicator of antioxidative capacity, in 16L:8D and 12L:12D treatments compared to 23L:1D and 20L:4D treatments [18]. Although antioxidant indices were not assessed in this study, current and previous data do support a positive impact of shorter photoperiod on oxidative stability. The photoperiod effect on oxidative reactions and antioxidative capacity of broiler meat would warrant further research.

The ratio of oxidized (GSSG) to reduced (GSH) glutathione, known as the glutathione redox ratio, has been used as a reliable marker of oxidative stress, based on the role of GSH in protecting against

free radicals [40,41]. In the present study, UPLC–MS metabolomics tentatively identified GSSG as higher in the fillet muscles of 20L:4D compared to 12L:12D. Previously, Guob et al. [18] assessed activity of serum GSH peroxidase and found no significant relationship between its activity and photoperiod. However, Asensi et al. [40] reported a positive relationship between the glutathione redox ratio and buildup of lactate/pyruvate through exhaustive physical exercise. Thus, it is reasonable to postulate that postmortem muscle metabolism might have been altered between the 20L:4D and 12L:12D groups. The problem of pale, soft, exudative (PSE) meat in broilers has been well-documented, with findings of higher muscle CIE L\*, poorer WHC, and poorer protein functionality [42,43]. The PSE condition arises from rapid glycolysis during early postmortem, causing rapid muscle acidification when the carcass is not chilled, resulting in a denaturation of muscle proteins. Given the higher CIE L\* in 20L:4D early in display coupled with trends of higher moisture loss during carcass chilling (*p* = 0.070) and a great transmission value (*p* = 0.058), it would be reasonable to postulate that longer photoperiods might contribute to the PSE-like condition in broiler fillet muscles. However, given the lack of differences in pH, other measures of WHC, and metabolites related to glycolytic pathways, the hypothesis would need to be tested in further studies.

In this study, several tentatively identified metabolites with important biological functions, including guanine, methylated guanosine, and dimethylguanosine, were upregulated in 20L:4D fillet muscles compared to 12L:12D. It has been implicated that hypermethylation of purine bases is a biological marker of disrupting tumor-suppressor genes and inactivating DNA repair genes [44]. In fact, Asensi et al. [40] identified a positive relationship between glutathione redox ratio and oxidative damage of DNA. Adding to this, the current results indicated that there was an upregulation of methylhistidine in 20L:4D. A positive relationship between skeletal muscle mass [45] and turnover of myofibrillar protein [46,47] with methylhistidine has been demonstrated in humans. As broilers grow, both an increase in absolute rates of breast muscle protein synthesis and degradation is observed, leading to an overall net increase in protein deposition [48]. Thus, there is some evidence to suggest 20L:4D treatment might alter muscle metabolome in a way that would support the rapid deposition of fillet muscle tissue.

l-Phenylalanine, tryptophan-leucine/isoleucine dipeptide, and tyrosine-leucine/isoleucine dipeptide were tentatively identified and found to be upregulated in the fillet muscles from 12L:12D broilers. l-Phenylalanine and l-tyrosine are known to be the precursors for catecholamine neurotransmitters, including dopamine, epinephrine, and norepinephrine [49]. Particularly, l-tyrosine has a positive impact on reducing levels of stress hormones [50] and ameliorates negative effects of sleep deprivation [51,52], and l-tryptophan has been well-established as a precursor for melatonin and serotonin [53,54]. Melatonin has a key role in regulation of circadian rhythm, and its production is suppressed by light exposure [55,56]. Given this relationship of these amino acids to stress and diurnal rhythm, the present study provides some evidence for a mechanism of extreme lengths of photoperiods on increasing oxidative stress.

#### **5. Conclusions**

Photoperiod treatments had minimal impacts on the carcass and meat quality traits of broiler fillet muscles. However, color and oxidative stability were influenced by the current photoperiod treatments and aerobic display storage. In general, the 20L:4D treatment fillets appeared to be lighter and more discolored, distinct from other photoperiod treatments during early display. This was coupled with a higher lipid oxidation in 20L:4D and 18L:6D treatments compared to 12L:12D. Metabolomic analyses indicated that compared to 12L:12D, the 20L:4D group exhibited a downregulation of aromatic amino acids, known to be related to neurotransmitter production, and an upregulation of oxidized glutathione, a biomarker of oxidative stress. These findings support a potential mechanism for the generation of long photoperiod-associated oxidative defects. For practical implications, the results of this study could provide valuable information and practical insights for the poultry industry to develop some

pre- and post-harvest strategies for minimizing any quality defects of fresh meat products from broilers exposed to extended photoperiod environments.

**Author Contributions:** Conceptualization, H.-W.C. and Y.H.B.K.; Methodology, J.R.T., T.S., W.W., H.-W.C., and Y.H.B.K.; Software, J.R.T., T.S., and B.C.; Validation, H.-W.C. and Y.H.B.K.; Formal Analysis, J.R.T. and T.S.; Investigation, J.R.T., J.-Y.P., W.W., and B.C.; Resources, W.W. and H.-W.C.; Data Curation, J.R.T., T.S., and B.C.; Writing—Original Draft Preparation, J.R.T.; Writing—Review and Editing, J.R.T., H.-W.C., and Y.H.B.K.; Visualization, J.R.T. and T.S.; Supervision, H.-W.C. and Y.H.B.K.; Project Administration, H.-W.C. and Y.H.B.K.; Funding Acquisition, H.-W.C. and Y.H.B.K. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Acknowledgments:** Authors would like to thank Erin Will of the Purdue Meat Science and Muscle Biology Laboratory, Jason Fields of the Animal Sciences Research and Education Center Poultry Unit at Purdue University, and Blaine Brown and Gary Waters of the Purdue Boilermaker Butcher Block for their assistance in sample collection and processing. This research did not receive external funding. Specification of commercial products or trade names does not imply recommendation or endorsement by the USDA. The USDA is an equal opportunity provider and employer.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

#### *Article*


**1. Introduction**

Over the past three decades, the emergence of small portable lab-on-a-chip biosensors has developed into an important area in biology and medicine [1]. Lab-on-a-chip devices promise to perform all the functions of a traditional laboratory on a miniature microfluidic platform [2]. These small devices allow full automation of analysis, reduce the sample volumes, and reduce the time of analysis as well [2] SAW devices are ideally suited to provide the sensing element in the Lab-on-a-chip platform [3]. One limitation that has plagued lab-on-a-chip devices has been the lack of a miniaturized sensor that facilitates the development of high-sensitivity assays. Several technologies have been explored including Mass spectrometry field [4, 5], electrochemical

sensors field [6], optical detection field [7, 8], surface plasmon resonance spectroscopy field [9], and interferometry [10, 11] but each has significant limitations in complex biological matrices. The SAW biosensor technology is based on Surface acoustic waves (SAW), a type of acoustic wave that propagates along a surface of a solid material [12–14]. Propagating SAW are impacted by mass loading and changes in viscoelastic properties of the media on the surface of the sensor and compared to a reference, as seen in **Figure 1a** and **b** [15]. These waves were first described by John William Strutt, 3rd (Lord Rayleigh), in an article on the propagation of acoustic waves in a piezoelectric material [16]. White and Voltmer introduced the concept of using interdigital transducers (IDTs) as a more efficient method of generating surface acoustic waves [17–19]. This method is still used today to generate surface acoustic waves on a piezoelectric material [20, 21]. The piezoelectric materials that are typically used for fabricating SAW devices are listed in **Table 1** [17].

To date, the primary use of SAW devices has been in the telecommunications industry, specifically as filters in cellular telephones and other smart devices [17]. SAW devices operate in the following manner: a metal IDT deposited on a piezoelectric surface is driven by a sinusoidal power source with a period specific to the IDT design [22]. This causes the electrode to vibrate, generating an acoustic wave that is perpendicular to the direction of the IDTs [23]. The penetration depth of the acoustic wave is relatively shallow. This is because the wave produces an evanescent field that cannot penetrate more than a few nanometers into the substrate from which the sensors are fabricated. If a guiding layer is used (a top layer), the acoustic wave propagation will be confined to the substrate-sample interface [24]. The confinement of the wave via a guiding layer maximizes the energy density of the acoustic wave at the substratesample interface.

The velocity of an acoustic wave in a piezoelectric material is an intrinsic property and varies slightly as a function of temperature [25–27] In the case of lithium tantalate, one of the more popular substrates used for the fabrication of SAW filters and devices, the wave velocity is 4200 m s−1 [28]. Values for the acoustic properties of commonly used piezoelectric substrates are listed in **Table 1** below. The acoustic wave velocity is approximately 104 –105 times smaller than the velocity of electromagnetic waves [29]. Surface acoustic waves can be generated and detected by spatially periodic, interdigital electrodes that are deposited on the planar surface of a piezoelectric plate. Excitation

#### **Figure 1.**

*Acoustic wave measurement using (a) a delay line SAW device with measurement performed via a two-port vector network analyzer (VNA). Connections are made using a customized printed circuit board with connectors to the VNA. In (b) there is a plot of the change in phase of the reference, the change in phase of the sample and the difference between the two channels.*

*Development of Simple and Portable Surface Acoustic Wave Biosensors for Applications… DOI: http://dx.doi.org/10.5772/intechopen.106630*

#### **Table 1.**

*Properties of common piezoelectric substrates used in the fabrication of surface acoustic wave devices. Courtesy of The Roditi International Corporation Ltd, UK.*

of the interdigitated electrode with a radio frequency source generates a periodic electric field, thus permitting piezoelectric coupling to a traveling surface wave. The center frequency of the acoustic wave generated by the sensor, fc, is governed by the Rayleigh wave velocity (VR). VR depends on the piezoelectric substrate and the electrode width (a) of a single finger, according to the equation fc = VR/4a [30]. The velocity for the SAW generated by the device depends on the properties of the piezoelectric substrate (crystal) that is used in the fabrication of the sensor and its crystallographic orientation. Computer models have allowed the careful sorting of numerous crystallographic orientations to enable the discovery of different types of acoustic waves. Where vs. is the SAW velocity and fc is the center frequency of the device. The SAW velocity is an important parameter determining the center frequency. The mass sensitivity is given by Sauerbrey's equation, where:

$$
\Delta \mathbf{f} / \mathbf{f}\_{\mathbf{o}} \approx \Delta \mathbf{V} / \mathbf{V}\_{\mathbf{o}} \tag{1}
$$

and

$$\mathbf{f}\_0 = \nu \wr \mathcal{X} \tag{2}$$

Propagation loss is one of the major factors that determine the insertion loss of a device and is caused by wave scattering at crystalline defects and surface irregularities. Materials that show high electromechanical coupling factors combined with small temperature coefficients of delay are generally preferred. The free surface velocity, Vf, of the material is a function of the cut angle and propagation direction. The TCD is an indication of the frequency shift expected for a transducer due to a temperature change and is also a function of the cut angle and propagation direction. The substrate is chosen based on the device design specifications, which include operating temperature, fractional bandwidth, and insertion loss.

Indeed, the fabrication of SAW devices requires a few critical components including the physical deposition of a metal on the surface of the piezoelectric substrate, etching of that metal deposited on the surface, optional deposition of guiding, and/or sensing layer. SAW devices mainly have two kinds of structures. The first is a design that features two sets of IDTs where a sinusoidal radio frequency (*RF*) is applied to one side of the IDT structure while the other is connected to the ground. The *RF* is applied to the first set of IDTs or the input IDTs, which generates an acoustic wave, as is seen in **Figure 1a**. The wave is transmitted through a delay line and is received by the second set of output IDTs. The signal is then captured and analyzed. The second type of SAW termed a resonator, features one set of IDTs with grating reflectors that can trap the surface wave. The signal from the IDT can be amplified and then fed back to the input IDT.

There are several factors that affect the transmission of the acoustic wave in a SAW device and have to be closely monitored or controlled in order to generate repeatable results. These include temperature, pressure, humidity, and mass loading. Indeed, SAW devices can be operated as sensors for temperature, pressure, humidity and mass loading. Temperature effects are the most challenging when trying to perform sensitive acoustic-wave sensing in liquid media. This is controlled in part by including a reference delay line. The reference is functionalized with a passivating agent to minimize nonspecific binding (e.g., PEG). For biological or medical applications, the active sensing line is functionalized with capturing agents that interact specifically with the analyte being targeted. An Analyte can be quantitatively detected by monitoring changes in gas pressure, liquid pressure or from the increase mass due to binding of a biological molecule to a targeting molecule or the increase in the fluid density or mass increases due to absorption of the target. The sensitivity of SAW devices increases as the square of the frequency; therefore, higher frequencies lead to smaller, more sensitive instruments. However, the frequency also determines the depth within the sample that the device can probe; for example, for a solution placed atop a sensor, the higher frequencies will examine a shallower depth than lower frequencies. Thus, the operating frequency of the SAW must be considered when selecting targets, probes, and conjugation schemes to functionalize the active sensing region of a SAW device.

Aside from the layer of targeting molecules that is typically used to decorate a SAW device, an additional guiding layer is often deposited to enhance the sensitivity of the device. The guiding layer traps the energy of the acoustic wave near the surface of the device to increase sensitivity to surface perturbations. The SAW sensors are inherently capable of detecting analytes in solution concentrations on the order of parts-per-billion (ppb) by mass, through the use of higher frequencies >300 MHz Ideally, the guiding layer needs to have a lower density and lower acoustic velocity than the piezoelectric substrate. Materials that have been utilized in past include polymers such as poly-methyl-methacrylate and Novolac and oxides including silicon dioxide and silicon monoxide. Changes in the mass loading of the surface, if all else is kept constant, affect the acoustic wave velocity as it travels across the delay line from the input IDT to the output IDT. The time delay is a result of the interactions between any adsorbed mass, i.e. the analyte, and results in a phase shift between the applied and the detected sinusoidal wave. In the case where there is only a single IDT, the round-trip time is measured by the applied signal. In recent years, there has been an increasing demand for portable, disposable and inexpensive sensors for biological and medical applications. Due to these increasing demands for miniature sensors, SAW devices have received renewed interest for use as sensors in biochemical assays and as detectors *Development of Simple and Portable Surface Acoustic Wave Biosensors for Applications… DOI: http://dx.doi.org/10.5772/intechopen.106630*

in microfluidic biosensors, particularly since it is a label-free technique. In this review, we will outline the use of SAW biosensors in biology and medicine.

## **2. Principles for SAW biosensors**

Surface acoustic waves are generated in a SAW device with the application of a sinusoidal *RF* to one side of the IDT patterned on the piezoelectric material, while the other side is connected to the ground. An image of a typical measurement scheme is shown in **Figure 1a** with a custom-built printed circuit board and connections with a vector network analyzer (VNA). The wavelength of the acoustic wave generated will be a function of the material properties, the shape, and layout of the IDTs, and the material deposited as the guiding layer. The main parameter utilized in data analysis is the velocity of the acoustic wave. Each piezoelectric material will allow an acoustic wave to propagate at different velocities for a given wavelength. The larger the wave velocity, the smaller time needed to have the acoustic wave travel from the input IDT to the output IDT. Another important parameter for SAW devices is the frequency that it operates. Only when the wavelength of the applied R*F* is equal to the intrinsic wavelength of the SAW, i.e. the period of the IDT, can the SAW be stimulated to produce an acoustic wave described by Eq. (2). Measurements of acoustic wave velocity is often reported as a phase shift between a reference channel and a sample channel. An image of a typical measurement is seen in **Figure 1a** while the plot of phase shift is seen in **Figure 1b**.

The IDTs can be fabricated as periodic bars with uniform lengths, widths, and gap spacing. They can be designed to give bi-directional or unidirectional acoustic wave propagation. Small IDT gap spacing and widths and thus smaller wavelengths often result in a higher frequency. In some cases, different IDT width/gap ratios can generate higher harmonic waves. The velocity of the wave that is generated can be influenced by several factors such as temperature and the analyte's concentration change. The temperature has a significant effect on the wave velocity. This effect can be described as the temperature coefficient of the frequency (TCF). TCF is described as a relative change in frequency with the temperature. Other factors that influence the device's sensitivity include humidity and pressure. Any slight fluctuations in the pressure will impact the mass loading of the device. Any change in the humidity results in changes to the electric field. The electric field strength is also impacted by charged particles that are suspended in the solution with comparatively high dipole moments (**Figure 2**).

#### **Figure 2.**

*Image of a surface acoustic wave biosensor where (a) is an image of new unused sensors while (b) shows an image of used sensors and (c) is a high magnification image of one of the sample channels and the reference channels from (b).*


*2.4. N-Mode Product*

It means the multiplication of a tensor A ∈ R*I*1×···×*IN* by a matrix **U** ∈ R*J*×*In* or vector **u** ∈ R*In* in mode *n*; i.e., along axis *n*. It is represented by B = A ×*n* **U**, where B ∈ R*I*1×···×*In*−1×*J*×*In*+1×···×*IN* [17].

#### *2.5. Rank-One Tensor*

A tensor X ∈ R*I*1×···×*IN* is rank one if it can be written as the outer product of *N* vectors; i.e., X = **a**(**1**) ◦···◦ **a**(**N**).

#### *2.6. Rank-R Tensor*

The rank of a tensor rank(X) is the smallest number of components in a CPD; i.e., the smallest number of rank-one tensors that generate X as their sum [17].

#### *2.7. N-Rank*

The *n*-rank of a tensor X ∈ R*I*1×···×*IN* denoted rank*n*(X), is the column rank of **X**(*n*); i.e., the dimension of the vector space spanned by the mode-*n* fibers. Hence, if *Rn* ≡ rank*n*(X) for *n* = 1, ... , *N*, we can say that X has a rank − (*R*1,..., *RN*) tensor.

All the tensor algebra notation presented until this point is summarized in Table 2 for simpler regarding.

**Table 2.** Tensor algebra notation summary

### *2.8. Tucker Decomposition (Tkd)*

The TKD can be seen as a form of higher-order PCA [17]. This method decomposes a tensor X ∈ R*I*1×···×*IN* into a core tensor G ∈ R*J*1×···×*JN* multiplied by a matrix along each mode *n* = 1, ... , *N* as

$$\mathfrak{X} \approx \mathfrak{Y} \times\_1 \mathfrak{U}^{(1)} \cdot \cdots \times\_N \mathfrak{U}^{(N)} \tag{1}$$

where the core tensor preserves the level of interaction for each factor or projection matrix **U**(*n*) ∈ R*In*×*Jn* . These matrices are usually, but not necessarily, orthogonal, and can be thought of as the principal components in each mode [17] (see Figure 1). *Jn* represents the number of components in the decomposition; i.e., the rank − (*R*1, ... , *RN*). We compute rank − (*R*1, ... , *RN*), where rank*n*(X) = *Rn* for every *n*-mode, which generally does not exactly reproduce X. Starting from (1), the reconstruction of an approximated tensor can be given by where Xˆ is the reconstructed tensor. Then, we can acquire the core tensor G by the multilinear projection

$$\mathfrak{G} = \mathfrak{A}^\* \times\_1 \mathbf{U}^{(1)T} \cdot \cdots \times\_N \mathbf{U}^{(N)T} \,. \tag{2}$$

where **U**(*n*)T denotes the transpose matrix of **U**(*n*) for *n* = 1, . . . , *N*. The reconstruction error *ξ* can be computed as

$$\xi(\mathfrak{X}) = ||\mathfrak{X} - \mathfrak{X}||\_{F'}^2 \tag{3}$$

where || · ||*F* represents the Frobenius norm. To effectively compress data, the reconstructed lower-rank tensor Xˆ should be close to the original tensor X; this can be reached by an algorithm as HOOI, which is iterative, and it is described in Section 5.1.

$$\mathbf{\hat{X}} = \mathbf{\hat{g}} \times\_1 \mathbf{U}^{(1)} \cdot \cdots \times\_N \mathbf{U}^{(N)},\tag{4}$$

**Figure 1.** Tucker decomposition for a third-order tensor.

#### **3. Problem Statement and Mathematical Definition**

Spectral images are third-order arrays, which provide not only spatial, but also spectral features from RS scenes of interest. These properties aid CNNs to easily find features to characterize the behaviors of different materials over the earth's surface. However, the large amount of spectral data causes huge computational load, and therefore, large processing time using machine learning algorithms.

It is important to preserve the three-dimensional array structure of the RS spectral input image, in order to effectively classify each pixel of the image. In RS multi- or hyperspectral images, the spectral bands are highly correlated, and contain lot of redundancy. Therefore, we propose a TKD-based method as a preprocessing step to provide a better suited input for the semantic segmentation based on CNN. This will also considerably reduce high number of parameters, and in turn, processing time during training and testing. Our problem statement for RS spectral images can be described as follows.

#### *3.1. Problem Statement*

Given a pair (X, **Y**), where tensor X ∈ R*I*1×*I*2×*I*3 denotes a CNNMSI or HSI, and **Y** ∈ R*I*1×*I*2 its corresponding ground truth matrix for a specific number of classes *C*, find another pair (G, **Y**ˆ), where the tensor G ∈ R*J*1×*J*2×*J*3 , used for classification, is representative of X, and **Y**ˆ is its associated matrix of predicted classes; preserving the spatial-domain *J*1 = *I*1, *J*2 = *I*2 but with fewer new tensor bands, i.e., *J*3 < *I*3, achieving higher or competitive performance metrics for pixel-wise classification, reducing the dimensionality, and therefore, decreasing computational complexity in the classification task.

### *3.2. Mathematical Definition*

We can describe the problem stated in previous subsection mathematically as the following optimization problem

$$\begin{array}{ll}\underset{\mathbf{(}\mathbf{U}^{(1)},\mathbf{U}^{(2)},\mathbf{U}^{(3)})}{\min} & ||\mathfrak{X}-\mathfrak{G}\times\_{1}\mathbf{U}^{(1)}\times\_{2}\mathbf{U}^{(2)}\times\_{3}\mathbf{U}^{(3)}||\_{F}^{2} \\\\ \text{subject to} & \mathbf{U}^{(n)}\in St\_{I\_{n}\times I\_{n}} \quad \text{and} & St\_{I\_{n}\times I\_{n}}\equiv\{\mathbf{U}^{(n)}\in\mathbb{R}^{I\_{n}\times I\_{n}} \mid \mathbf{U}^{(n)\top}\mathbf{U}^{(n)} = \mathbf{I}^{(n)}\}, \\ & I\_{1}=I\_{1},I\_{2}=I\_{2} & \text{preserving the pixel domain}, \\ & I\_{3}$$

where *ψ* denotes an error threshold defined depending on the accuracy or performance metrics required for each application and *StIn*×*Jn* represents the Stiefel manifold [30]. Embedding G into the objective function, as Lathhauwer proved in [31] Theorems 3.1, 4.1, and 4.2, (5), can be written by the equivalent under the same constraints as (5).

$$\max\_{\mathbf{U}^{(1)}, \mathbf{U}^{(2)}, \mathbf{U}^{(3)}} ||\mathfrak{X} \times\_1 \mathbf{U}^{(1)\mathbf{T}} \times\_2 \mathbf{U}^{(2)\mathbf{T}} \times\_3 \mathbf{U}^{(3)\mathbf{T}}||\_F^2 \tag{6a}$$

$$\text{where} \quad \mathbf{\mathcal{G}} = \mathbf{\mathcal{X}} \times\_1 \mathbf{U}^{(1)\mathbf{T}} \times\_2 \mathbf{U}^{(2)\mathbf{T}} \times\_3 \mathbf{U}^{(3)\mathbf{T}} \tag{6b}$$

The subtensors G*in* of the core tensor G satisfy the all-orthogonality property [32], which establishes that two subtensors G*in*=*α* and G*in*=*β* are all-orthogonal

$$
\langle \mathfrak{G}\_{i\_n=a}, \mathfrak{G}\_{i\_n=\beta} \rangle = 0 \tag{7}
$$

for all possible values of *n*, *α*, and *β* subject to *α* = *β*, and the ordering property:

$$\|\|\mathbf{G}\_{i\_n=1}\|\|\_F \ge \|\|\mathbf{G}\_{i\_n=2}\|\|\_F \ge \cdots \ge \|\|\mathbf{G}\_{i\_n=I\_N}\|\|\_F. \tag{8}$$

Our optimization problem can be solved by several algorithms. In this work, the HOOI algorithm was selected (described in Section 5.1), due to its convergence and orthogonality performance. Once a tensor G is obtained, a classifier *f* that belongs to the hypothesis space *H* maps input data G into output data **Y**ˆ ; that is

$$
\hat{\mathbf{Y}} = f(\mathbf{g})\tag{9}
$$

where *f* is a pixel-wise classifier. In this paper, a FCN for semantic segmentation was used as classifier due to the need of classify each pixel of the input image and to its performance in pixel accuracy. The FCN used in this work is described in Section 4.

#### **4. Convolutional Neural Networks (CNNs)**

CNNs are supervised feed-forward DL-ANNs for computer vision. The idea of applying a sort of convolution of the synaptic weights of a neural network through the input data yields to a preservation of spatial features, which alleviates the hard task of classification and in turn semantic segmentation. This type of ANN works under the same linear regression model as every machine learning (ML) algorithm. Since images are three dimensional arrays, we can use tensor algebra notation to describe the input of CNNs as a tensor A ∈ R*I*1×*I*2×*I*3 , where *I*1, *I*2, and *I*3 represent height, width, and depth of the third order array respectively; i.e., the spatial and spectral domain of an image. We can write generally the linear regression model used for ANNs as

$$
\hat{\mathbf{y}} = \sigma \left( \mathbf{W} \mathbf{g} + \mathbf{b} \right) \tag{10}
$$

where **y**ˆ represents the output prediction of the network; *σ* denotes an activation function; **g** is the input dataset; **W** and **b** are the matrix of synaptic weights and the bias vector, respectively. These parameters are adjustable; i.e., their values are modified every iteration looking for convergence to minimize the loss in the prediction through optimization algorithms [33]. For simplicity, the bias vector can be ignored, assuming that matrix **W** will update until convergence independently of another parameter [33]. Considering that the input dataset to a CNN is a multidimensional array, we can represent (9) and (10) using tensor algebra notation as

$$
\mathfrak{G} = \sigma \left( \mathfrak{W} \mathfrak{G} \right) \tag{11}
$$

where Yˆ represents the prediction output tensor of the ANN (in our case, a second order tensor or matrix **Y**ˆ ), G is the input dataset, and W is a *K*1 × *K*2 × *F*1 tensor called filter or kernel with the adaptable synaptic weights. Different to conventional ANN, in CNNs, W is a shiftable square tensor is much smaller in height and width than the input data, i.e., *K*1 = *K*2 and *Ks* << *Is* for *s* = 1, 2; *F*1 denotes the number of input channels; i.e., *F*1 = *I*3. For hidden layers, instead of the prediction tensor Yˆ, the output is a matrix called activation map **M** ∈ R*I*1×*I*2 , which preserves features from the original data in each domain. Actually, it is necessary to use much kernels W(*f*2) as activation maps, with different initialization values to preserve diverse features of the image. Hence, we can also define activation maps as a tensor M ∈ R*I*1×*I*2×*F*2 where *F*2 denotes the number of activation maps produced by each filter (see Figure 2). Kernels are displaced through the whole input image as a discrete convolution operation. Then, each element of the output activation map *mi*1*i*2 *f*2 is computed by the summary of the Hadamard product of kernel W(*f*2) and a subtensor from the input tensor G centered in position (*i*, *j*) and with same dimensions of W, as follows

$$m\_{i\_1 i\_2 f\_2} = \sigma \left[ \sum\_{k\_1=1}^{K\_1} \sum\_{k\_2=1}^{K\_2} \sum\_{f\_1=1}^{F\_1} w\_{k\_1 k\_2 f\_1} \xi\_{i\_1 + k\_1 - o\_1, i\_2 + k\_2 - o\_2, f\_1} \right] \tag{12}$$

where *mi*1*i*2 *f*2 denotes the value of the output activation map *f*2 at position *i*1, *i*2; *σ* represents the activation function; and *o*1 and *o*2 are offsets in spatial dimensions which depend on the kernel size, and equal *K*1+1 2 and *K*2+1 2 respectively (see Figure 2).

**Figure 2.** Convolutional layer with a *K*1 × *K*2 × *F*1 × *F*2 kernel. Input channels *F*1 must equal the spectral bands *I*3. To preserve original dimensions at the output, zero padding is needed [18]. Output dimensions also depend on stride *S* = 1 to consider every piece of pixel information and to preserve original dimensions.

An ANN is trained by using iterative gradient-based optimizers, such as Stochastic gradient descent, Momentum, RMSprop, and Adam [33]. This drive the cost function *L*(W) to a very low value by updating the synaptic weights W. We can compute the cost function by any function that measures the difference between the training data and the prediction, such as Euclidean distance or cross-entropy [10]. Besides, the same function is used to measure the performance of the model during testing and validation. In order to avoid overfitting [33], the total cost function used to train an ANN combines one of the cost functions mentioned before, plus a regularization term.

$$J(\mathbf{\mathcal{W}}) = L(\mathbf{\mathcal{W}}) + R(\mathbf{\mathcal{W}}),\tag{13}$$

where *J*(W) denotes the total cost function and *R*(W) represents a regularization function. Then, we can decrease *J*(W) by updating the synaptic weights in the direction of the negative gradient. This is known as the method of steepest descent or gradient descent.

$$\mathbf{W}' = \mathbf{W} - a\nabla\_{\mathbf{W}} I(\mathbf{W}),\tag{14}$$

where W represents the synaptic weights tensor in next iteration during training, *α* denotes the learning rate parameter, and ∇W *J*(W) the cost function gradient. Gradient descent converges when every element of the gradient is zero, or in practice, very close to zero [10].

CNNs has been successfully used in many image classification frameworks. This variation in architecture from other typical ANN models yields the network to learn spatial and spectral features, which are highly profitable for image classification. Besides, FCNs, constructed with only convolutional layers are able to classify each element of the input image; i.e., they yield pixel-wise classification, or in other words, semantic segmentation.

### **5. Hooi-Fcn Framework**

In this work we propose a TKD-CNN-based framework called HOOI-FCN, which maps the original high-correlated spectral image into a low-rank core tensor, preserving enough statistical information to alleviate image pixel-wise classification. The aim is to improve performance while reducing processing time in semantic segmentation ANNs by compressing CNNMSI third-order tensors. Applying TD methods, relevant information is preserved, mainly acquired from the spectral domain, convenient for the classification FCN. This novel framework is in summary, a two step structure composed by an HOOI TD and a FCN for semantic segmentation described below (see Figure 3).

### *5.1. Higher Order Orthogonal Iteration (HOOI) for Spectral Image Compression*

Quoting Kolda, "The truncated higher order singular value decomposition (HOSVD) is not optimal in terms of giving the best fit as measured by the norm of the difference, but it is a good starting point for an iterative alternating least square algorithm" [17]. HOOI is an iterative algorithm to compute a rank-(*R*1, ... , *RN*) TKD. Let X ∈ R*I*1×···×*IN* be an *N*-th order tensor and *R*1, ... , *RN* be a set of integers satisfying 1 ≤ *Rn* ≤ *In*, for *n* = 1, ... , *N*; the rank − (*R*1, ... , *RN*) approximation problem is to find a set of *In* × *Rn* matrices **U**(*n*) column-wise orthogonal and a *R*1 ×···× *RN* core tensor G by computing

$$\min\_{\mathbf{g}, \mathbf{U}^{(1)}, \dots, \mathbf{U}^{(N)}} ||\mathbf{\mathcal{X}} - \mathbf{\mathcal{G}} \times\_1 \mathbf{U}^{(1)} \cdot \dots \cdot\_N \mathbf{U}^{(N)}||^2 \tag{15}$$

and from matrices **U**(*n*), where **U**(*n*)T**U**(*n*) = **I**(*n*), the core tensor G is found to satisfy (2) [34]. For a third-order tensor decomposition, we can rewrite (4) as

$$\mathfrak{A} = \mathfrak{g} \times\_1 \mathbf{U}^{(1)} \times\_2 \mathbf{U}^{(2)} \times\_3 \mathbf{U}^{(3)} \tag{16}$$

where Xˆ denotes the reconstruction approximation of the input spectral image X, G is the *J*1 × *J*2 × *J*3 core tensor, and **U**(1) ∈ R*I*1×*J*1 , **U**(2) ∈ R*I*2×*J*2 and **U**(3) ∈ R*I*3×*J*3 are the projection matrices. Algorithm 1 shows HOOI for a third order tensor decomposition, but the extension to higher order tensors is straightforward. Thus, with Algorithm 1 we compute the tensor G with rank-(*J*1, *J*2, *J*3) for each spectral image as third-order tensor.

**Figure 3.** The big picture of the fast semantic segmentation framework proposed, with a fully convolutional network encoder-decoder architecture and a preprocessing HOOI tucker decomposition stage.

#### **Algorithm 1:** HOOI for MSI. ALS algorithm to compute the core tensor G.

**Function** HOOI(X*, R*1*, R*2*, R*3)**:** initialize **U**(*n*) ∈ R*In*×*Rn* for *n* = 1, 2, 3 using HOSVD; **repeat for** *n* = 1, 2, 3 **do** D ← X ×1 **U**(1)T ×2 **U**(2)T ×3 **U**(3)T **U**(*n*) ← *Rn* leading left singular vectors of **D**(*n*) **end until** *fit ceases to improve or maximum iterations exhausted*; G ← X ×1 **U**(1)T ×2 **U**(2)T ×3 **U**(3)T **Output:** G, **U**(1), **U**(2), **U**(3)

#### *5.2. Fcn for Semantic Segmentation of Spectral Images*

We use a FCN model for semantic segmentation based on the proposed by Badrinarayanan et al. in [35] called Segnet. Each core tensor G obtained after decomposition, is the input to the SegNet for training and testing the network. Hence, the feature activation maps M ∈ R*I*1×*I*2×*F*2 for each hidden layer of the SegNet encoder-decoder FCN are computed by displacing the filters W through the whole input core tensor in strides *S* = 1. It is worth noting that kernel W is a four-order tensor W ∈ R*K*1×*K*2×*F*1×*F*2 , where *K*1 and *K*2 represent its spatial dimensions height and width; *F*1 its depth, i.e., the spectral domain; and *F*2 denotes the number of filters used to produce *F*2 activation maps (Figure 2). We express this convolution operation as

$$\mathbf{M}^{(f\_2)} = \sigma \left( \mathbf{W} \odot \mathbf{\hat{g}} \right), \tag{17}$$

where **M**(*f*2) represents each activation map for *f*2 = 1, ... , *F*2, and each value *mi*1*i*2 *f*2 is computed as in (12). *σ* denotes the rectified linear unit (ReLU) [33] function; i.e., *σ*(*z*) = max {0, *z*}. Symbol is used in this paper to represent the convolution; i.e., the whole operation applied in convolutional layers (see Figure 2). These activation maps are the input for the subsequent layer in the SegNet FCN.

The last layer is used the softmax activation function [33] to produce a distribution probability, and so, predict values relating each pixel to one of the *C* classes of interest. Hence, for the last layer we rewrite (17) as

$$
\hat{\mathbf{Y}} = \delta \left( \mathbf{W} \odot \mathbf{M} \right),
\tag{18}
$$

where **Y**ˆ represents the output prediction, M is the feature activation maps at previous layer, *δ* the softmax activation function, and W the filter or kernel tensor with the adaptable synaptic weights.

The output of the FCN is a matrix **Y**ˆ with the same spatial dimensions as the input, with a value of the most likely class for each pixels. Figure 4 shows the architecture of the SegNet model used in this work. Experiments present the behavior of this FCN with and without data compression in the spectral domain.

**Figure 4.** SegNet FCN. Encoder-decoder architecture with convolutional, pooling, and upsampling layers with their corresponding activation functions and batch normalization [33].

#### **6. Experimental Results**

#### *6.1. Our Data*

As case study, a CNNMSI dataset with 100 RS images was used for training and 10 for testing, all of them from central Europe with 128 × 128 pixels. These images are partitions of the original Sentinel-2 images without modification and all semi-manually labeled, and with abundant presence of the elements of interest. In Table 3 the 10 scenarios correspond to our 10 images for testing. We used only nine from the 13 available spectral bands from visible, NIR to SWIR wavelengths. Bands 2, 3, 4, and 8 have 10 m resolution, and bands 5, 6, 7, 11, and 12 have 20 m (oversampled to 10 m [18]). These bands provide decisive information for discrimination of different classes. Bands 1, 9, and 10 were dismissed because of their lower spatial resolution of 60 m. Band 8A, also with 20 m spatial resolution, was dismissed due to wavelength overlapping with band 8. It is worth mentioning that the framework proposed in this work can be applied to any kind of spectral image and multitemporal datasets [36].

#### 6.1.1. The Training Space

For training, the input data ws a tensor X ∈ R128×128×9×100, where 128 × 128 is the spatial dimensions, 9 is the number of spectral bands, and 100 is the number of images used for training. Although the number of images seems low, taking into account that we work at pixel-domain, the real number of training points or vectors is high. Indeed, our FCN for semantic segmentation was trained with 128 × 128 × 100 = 1638400 samples or vectors. To test whether the size of the data for training was sufficiently high, a smaller subtensor of X, X*p* ∈ R128×128×9×80, equivalent to 1310720 points or vectors, was used for a second training obtaining, for the same test set, an average PA of 91.48%; i.e., only 0.08% less than with 100 images, 91.56%. We also tested these results by a third training with an extended dataset of 120 images, X*q* ∈ R128×128×9×120 equivalent to 1966080 vectors, and we found only a slight variation of +0.01% in the PA (91.57%), while the execution time for the training increased significantly.

#### 6.1.2. The Labels

Our labels were acquired using the scene classification algorithm developed by the ESA [19], and subsequently modified, semi-manually, misclassified pixels.